Digital threads and digital twins are used to create the virtual environments needed to implement model-based system engineering (MBSE), especially for complex cyber-physical systems. Digital threads are complete records of all details of specific aspects of product definition, development, and deployment, from conception through end of life. A digital twin is a comprehensive virtual representation of a physical object or system that spans its lifecycle, is updated from real-time data, and tracked by the digital thread. Digital twins are used for simulation and virtual testing of systems under development.
This FAQ looks at how digital threads and digital twins support MBSE, presents several levels of digital twin sophistication, reviews how digital twins benefit from integration into the Internet of Things (IoT), and closes by considering how machine learning (ML) can be used to further leverage the power of digital twins in MBSE.
Digital threads provide digitized traceability from product definition and conceptualization through end-of-life (Figure 1). Digital threads can include multiple digital strands. There can be digital strands for quality engineering (including safety, security, and reliability engineering), continuous design verification and validation, integrated program planning and execution, product lifecycle management, and so on. Those strands are linked with each other and with the physical world to create a complete digital thread.
Digital threads comprise all digital twins’ temporal details, including design data, simulation, testing and performance data, software and hardware development strands, supply chain data, production data, and actual performance data after the product is used. Digital threads were initially developed to support MBSE, but their utility has been expanded. For complex systems, there is often a unique digital thread associated with each system built, such as individual aircraft. These threads are used for managing product field performance and reliability. They can provide valuable data that can be incorporated into future MBSE-based design, engineering, production, and deployment efforts, accelerating subsequent development processes, reducing costs, and producing more robust results.
Digital threads tie the MBSE process together by embodying all data related to digital twins. Digital twins provide a common description of the system that can be accessed by various design disciplines, such as electric, electronic, mechanical, software, etc. The digital twin eliminates paper-based documentation and provides a single source of ‘truth’ about the system and its development status. It breaks down engineering ‘silos’ and provides all stakeholders access to a complete digital model of the system, including all simulation, testing, and performance data.
A digital twin is dynamic through time, even after the system enters production, and the corresponding digital thread captures the history of all changes to the digital twin. The combined digital twin and digital thread comprise a complete system history.
Digital twin levels of sophistication
The term digital twin is widely used to denote any digital version of a system, device, or component. But not all digital twins are created equal. There are several levels of sophistication, or maturity, associated with digital twins (Table 1):
- Level 1 is a basic virtual prototype. This ‘pre-digital twin’ can support the conceptualization and preliminary design stages of MBSE and is created before any physical prototype exists. The main use of a level 1 pre-digital twin is to identify technical risks that need to be addressed before detailed engineering development can begin.
- Level 2 is a basic digital twin and includes a comprehensive virtual system model that can integrate performance, maintenance, and other data. It will typically be updated periodically with batches of data from the physical twin. It can be used for refining technology specifications, conceptual design efforts, and early-stage design and development.
- Level 3 digital twins are adaptive to the needs and preferences of users. The digital twin interface can be modified by individual users, making it more useful for the various engineering disciplines involved in the system design process. Some level 3 digital twins use machine learning algorithms to identify user preferences and automatically adapt. These adaptions can be context sensitive, and the twin can appear differently to a single user depending on the activities pursued.
- Level 4 digital twins support the adaptive capabilities of level 3 and add a higher degree of autonomy. In addition to accommodating the preferences of users, level 4 digital twins use machine learning to identify objects and patterns in the environment and analyze performance data received from the physical twin. Depending on the sophistication of the ML algorithms, level 4 digital twins can incorporate partially observable environments and varying levels of uncertainty in the data. These twins can be updated in real-time and via batches of data.
Development of increasingly sophisticated digital twins can be especially important for supporting high-fidelity simulations. As systems move through the development process, the importance of accurate and timely simulations grows. Early in the conceptualization and design process, simulations explore various application scenarios, potential technology deficiencies, and system architectures, not real-time performance. A relatively simple digital twin can support those activities. Later in the design process, real-time operation simulations under actual operational conditions become important, and a more sophisticated digital twin implementation is needed.
Digital strands, threads, twins, and the IoT
Linking digital strands, threads, and digital twins to the physical world using the IoT can further enhance the real-time capabilities of MBSE. Multiple digital strands for quality engineering (including safety, security, and reliability), continuous design verification and validation, integrated program planning and execution, and even product lifecycle management can be linked with each other and the physical world using the IoT. Linking digital twins with the real world using the IoT can speed the integration of updated performance data and result in a more rapid realization of increasingly realistic digital twins. These IoT links can include performance data gathered after the system has been delivered to enhance preventative maintenance, safety functions, and other aspects of system performance even after the system is in the field (Figure 2).
Digital twins and ML
For basic systems, such as a simple electric vehicle, digital twins without ML can bring significant value to the MBSE process. These applications tend to have a limited and manageable set of performance variables and relatively simple relationships between the system and the environment. More complex systems, such as vehicles with varying autonomous operating capabilities or smart buildings with multiple interrelated systems, can benefit from adding ML into the digital twin and MBSE process. In complex situations with multiple semi-independent or fully independent processes, ML can more quickly analyze data streams to identify patterns or trends that can be incorporated into the MBSE process.
ML can automate routine, but complex tasks, evaluate incoming data in real time, make adjustments in response to changing information, and increase the speed of arriving at the required system performance. ML can also be used to implement unsupervised simulations and testing of digital twins, freeing designers to undertake more creative tasks related to optimization.
Summary
Digital threads and digital twins are basic concepts in MBSE. A digital thread can be composed of multiple digital strands related to specific design functions. The combined thread provides a complete history of developing a digital twin and the corresponding physical system. The digital twin is a complete digital representation of the physical world. It eliminates the need for paper reporting and documentation and provides a common point of reference for all the design teams needed to implement MBSE. A digital twin can be embodied with varying levels of sophistication to suit specific stages in the development process. Simple digital twins can be used to develop initial design concepts and architectures. More complex digital twins can support real-time simulations of completed systems under actual operating conditions.
References
Integrated Model-based Systems Engineering (iMBSE) in Engineering Education, Purdue School of Engineering and Technology
Leveraging Digital Twin Technology in Model-Based Systems Engineering, MDPI systems
What does a digital thread mean, and how it differs from digital twin, Challenge Advisory
