Digital Twin

What is a digital twin?

A digital twin is a virtual representation or digital counterpart of a physical object, system or process. It is created using real-time data, simulation and modeling techniques to mirror the behavior, characteristics and performance of its physical counterpart. Digital twins are used across various industries, including manufacturing, healthcare, transportation and energy, to optimize performance, monitor operations and facilitate decision-making.

Related products: NX CAD | Simcenter Simulation Software | Solid Edge

Key characteristics of digital twins

Real-time data integration:

Digital twins are continuously updated with real-time data from sensors, IoT devices, and other sources, providing an accurate representation of the physical asset or system at any given time.

Simulation and modeling:

Digital twins often incorporate simulation and modeling techniques to simulate the behavior and performance of the physical asset or system under different conditions. This allows for predictive analysis, optimization and scenario planning.

Bi-directional communication:

Digital twins enable bi-directional communication between the virtual model and its physical counterpart. This means that data and insights from the digital twin can inform decisions and actions in the physical world, and vice versa.

Monitoring and control:

Digital twins allow for real-time monitoring and control of the physical asset or system from a virtual environment. This enables remote monitoring, diagnostics and predictive maintenance to optimize performance and reduce downtime.

Lifecycle management:

Digital twins support the entire lifecycle of a product or system, from design and development to operation and maintenance. They can be used for design validation, testing, training and even decommissioning.

Collaborative environment:

Digital twins facilitate collaboration among different stakeholders, such as engineers, operators and maintenance personnel, by providing a common platform for data sharing, analysis and decision-making.

Examples of digital twins in the industry

Manufacturing:

Digital twins of manufacturing processes and equipment can optimize production schedules, predict equipment failures and improve overall efficiency.

Smart cities:

Digital twins of urban infrastructure, such as transportation networks and utilities, can optimize traffic flow, manage energy consumption and enhance public services.

Healthcare:

Digital twins of patient physiology and medical devices can support personalized treatment plans, monitor health metrics remotely and simulate surgical procedures.

Energy:

Digital twins of power plants and renewable energy systems can optimize energy production, predict equipment failures and manage grid stability.

Overall, digital twins offer a powerful framework for gaining insights, optimizing performance and driving innovation across various industries by bridging the gap between the physical and digital worlds.

By incorporating multi-physics simulation, data analytics and machine learning capabilities, digital twins are able to demonstrate the impact of design changes, usage scenarios, environmental conditions and other endless variables – eliminating the need for physical prototypes, reducing development time and improving the quality of the finalized product or process.

To ensure accurate modeling over the entire lifetime of a product or its production, digital twins use data from sensors installed on physical objects to determine the objects’ real-time performance, operating conditions, and changes over time. Using this data, the digital twin evolves and continuously updates to reflect any change to the physical counterpart throughout the product lifecycle, creating a closed loop of feedback in a virtual environment that enables companies to continuously optimize their products, production, and performance at minimal cost.

The potential applications for a digital twin depend on what stage of the product lifecycle it models. Generally speaking, there are three types of digital twins – Product, Production, and Performance, which are explained below. The combination and integration of the three digital twins as they evolve together is known as the digital thread. The term "thread" is used because it is woven into, and brings together data from, all stages of the product and production lifecycles.

Three types of digital twins

1. Product digital wins: Product digital twins replicate physical products in a digital form. They are used in product design, testing, and simulation. Product digital twins help engineers and designers analyze how a product will perform under different conditions, enabling them to optimize its design and functionality before physical production begins.
2. Process digital twins: Process digital twins simulate and analyze the behavior of physical processes or systems. They are used to monitor, control, and optimize the operations of complex systems such as manufacturing plants, supply chains, and energy grids. Process digital twins enable organizations to visualize, simulate, and analyze processes in real time, facilitating better decision-making and performance optimization.
3. System digital twins: System digital twins replicate entire systems or ecosystems in a digital environment. They integrate multiple digital twins of products, processes, and other components to simulate the behavior of complex systems comprehensively. System digital twins are used to model and analyze large-scale systems such as smart cities, transportation networks, and industrial complexes.

The Digital Twin in CAD and simulation software

Digital twin modeling can be part of both CAD (Computer-Aided Design) software and simulation software, depending on the specific functionalities and capabilities of the software in question.

1. CAD software

   CAD software is primarily used for creating detailed 3D models of physical objects or systems. In the context of digital twins, CAD software can be used to create the virtual representation or geometry of the physical asset. This includes modeling the geometry, structure, components, and assemblies of the physical object. CAD software typically focuses on geometric representation and design intent, allowing engineers to create accurate virtual models of products or systems.

2. Simulation software

   Simulation software, on the other hand, is used to simulate the behavior and performance of physical systems under various conditions. Simulation software can incorporate digital twin modeling by integrating real-time data, physics-based models, and simulation techniques to create a virtual representation of the physical asset. This includes simulating the dynamic behavior, interactions, and performance characteristics of the physical system. Simulation software focuses on analyzing and predicting the behavior of the system based on underlying physics principles.

In practice, digital twin modeling often involves a combination of CAD software and simulation software. CAD software is used to create the geometric representation of the physical asset, while simulation software is used to simulate the behavior and performance of the digital twin. The integration between CAD and simulation software allows engineers to create comprehensive digital twins that accurately represent the physical system and its dynamic behavior. Furthermore, some software platforms offer integrated solutions that combine CAD and simulation capabilities into a single platform, allowing users to seamlessly transition from design to analysis within the same environment. These integrated solutions enable engineers to create, simulate and optimize digital twins more efficiently and effectively.

Both Siemens NX and Siemens Solid Edge CAD software offer integrated solutions that combine CAD and simulation capabilities. NX is a comprehensive software suite developed by Siemens Digital Industries Software, which includes advanced CAD, CAM (Computer-Aided Manufacturing), CAE (Computer-Aided Engineering), and PLM (Product Lifecycle Management) functionalities. Within the NX ecosystem, there are integrated solutions that enable a seamless transition from CAD design to simulation analysis: Solid Edge is a comprehensive suite of CAD software tools developed by Siemens Digital Industries Software, designed primarily for product design, modeling, drafting and assembly management. Within the Solid Edge ecosystem, there are integrated solutions that facilitate a seamless transition from CAD design to simulation analysis:

NX CAD

NX CAD provides powerful tools for 3D modeling, assembly design, drafting, and visualization. Engineers can create detailed geometric models of products and systems using NX CAD's feature-rich environment. This serves as the foundation for digital twin modeling within the NX ecosystem. NX CAE: NX CAE (Computer-Aided Engineering) is the simulation and analysis module of NX, offering a wide range of capabilities for structural, thermal, fluid dynamics, motion, and multiphysics simulations. NX CAE allows engineers to simulate the behavior and performance of digital twins under various operating conditions and loading scenarios.

Integrated simulation environment:

NX provides an integrated simulation environment where engineers can seamlessly transfer CAD geometry to NX CAE for analysis. This integration streamlines the simulation workflow, allowing engineers to perform design validation, optimization, and analysis within the same software platform.

Multiphysics simulation

NX offers multiphysics simulation capabilities that enable engineers to simulate interactions between different physical phenomena, such as structural mechanics, fluid dynamics, thermal management, and electromagnetic effects. This allows for a comprehensive analysis of digital twins that involve multiple physics domains.

Design optimization

NX includes tools for design optimization and sensitivity analysis, allowing engineers to explore design alternatives, improve performance, and meet design objectives. Design changes made in CAD can be automatically updated in the simulation model, ensuring consistency and accuracy throughout the design process.

Overall, Siemens CAD software provides integrated solutions that combine CAD and simulation capabilities, enabling engineers to create, simulate, and optimize digital twins more efficiently and effectively. This integration enhances collaboration, streamlines workflows, and accelerates product development across various industries.

The Executable Digital Twin (xDT)

Unlike traditional digital twins, which are primarily used for monitoring and analysis, executable digital twins are active, dynamic models that can respond to inputs, simulate scenarios, and make decisions autonomously or with human intervention. The executable digital twin (or xDT). In simple terms, the xDT is the digital twin on a chip. The xDT uses data from a (relatively) small number of sensors embedded in the physical product to perform real-time simulations using reduced-order models. From those small numbers of sensors, it can predict the physical state at any point on the object (even in places where it would be impossible to place sensors).

Key characteristics of executable digital twins

Real-time simulation and interaction

Executable digital twins (xDT) are capable of simulating the behavior and performance of the physical asset or system in real time. They can respond to inputs, simulate different operating conditions, and interact with external systems or users dynamically.

Autonomy and decision-making

Executable digital twins (xDT) can make decisions autonomously based on predefined rules, algorithms or machine learning models. They can analyze data, predict outcomes and take actions to optimize performance or respond to changing conditions.

Closed-loop control

Executable digital twins (xDT) often operate in a closed-loop control system, where real-time data from sensors and actuators are fed back into the virtual model to adjust parameters, optimize performance and maintain desired operating conditions.

Predictive analysis and optimization:

Executable digital twins(xDT) use predictive analytics and optimization techniques to forecast future behavior, identify potential issues or opportunities and recommend actions to improve performance or mitigate risks.

Integration with IoT and AI technologies

Executable digital twins(xDT) leverage Internet of Things (IoT) sensors, connectivity and artificial intelligence (AI) algorithms to collect real-time data, analyze complex patterns and make informed decisions. They may also incorporate machine learning models for adaptive behavior and continuous improvement.

Dynamic adaptation and learning

Executable digital twins(xDT) are capable of learning from experience and adapting to changes in the environment or operating conditions over time. They can continuously update their models, parameters and strategies based on new data and feedback.

Executable digital twins find applications across various industries, including manufacturing, energy, transportation, healthcare and smart cities. They enable predictive maintenance, autonomous operation, optimization of processes and decision support in complex systems where real-time monitoring and control are critical. Overall, executable digital twins represent the next evolution in digital twin technology, offering enhanced capabilities for real-time simulation, decision-making and optimization of physical assets and systems. An executable digital twin is an advanced form of a digital twin that not only represents a virtual replica of a physical asset or system but also has the capability to execute, simulate and interact with the virtual model in real time.

The xDT is physics-based

A physics-based executable digital twin relies on mathematical models that describe the physical behavior of the system being replicated. These models are typically based on fundamental principles of physics, such as mechanics, thermodynamics, fluid dynamics, electromagnetics, and so on. By solving the equations that govern these physical phenomena, the digital twin can simulate the behavior of the real-world system in a virtual environment. Characteristics of a physics-based executable digital twin

Physics-based models

The digital twin incorporates physics-based models that accurately represent the behavior of the physical system. These models may include equations describing motion, heat transfer, fluid flow, electrical circuits, structural mechanics, and other physical phenomena relevant to the system being modeled.

Simulation of physical processes

The digital twin simulates the physical processes and interactions within the system using physics-based models. This allows it to predict how the system will behave under different operating conditions, inputs and scenarios.

Real-time simulation

An executable digital twin based on physics models can simulate the behavior of the physical system in real-time or near-real-time. This enables dynamic interaction and decision-making based on the current state of the system and its environment.

Closed-loop control

Physics-based executable digital twins often operate in a closed-loop control system, where real-time data from sensors and actuators are used to adjust the simulation parameters and control the behavior of the virtual model. This allows the digital twin to maintain desired operating conditions and optimize performance.

Validation and verification

Physics-based models used in executable digital twins must be validated and verified to ensure their accuracy and reliability. This involves comparing simulation results with real-world measurements and experimental data to confirm that the digital twin accurately represents the physical system.

While physics-based modeling is commonly used in executable digital twins, it's important to note that other modeling approaches, such as data-driven modeling, empirical models, or hybrid models combining physics and data-driven techniques, may also be employed depending on the specific requirements and constraints of the application.

Siemens Simcenter

Simcenter™ software uniquely combines system simulation, 3D CAE, and testing to help you predict performance across all critical attributes earlier and throughout the entire product lifecycle. By combining physics-based simulations with insights gained from data analytics, Simcenter helps you optimize design and deliver innovations faster and with greater confidence.

Engineering departments today must develop smart products that integrate mechanical functions with electronics and controls, utilize new materials and manufacturing methods and deliver new designs within ever shorter design cycles. This requires current engineering practices for product performance verification to evolve into a Digital Twin approach, which enables you to follow a more predictive process for systems-driven product development.

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