Abstract Digital twins couple computational models with physical systems to enable services such as predictive reasoning and runtime analysis that support decision–making for maintenance, optimisation and system evolution. They create a technically rich setting for applying formal methods throughout the system lifecycle beyond design-time. This tutorial introduces the elements of digital twin technology, identifying emerging opportunities for formal techniques, including the verification of evolving models under uncertainty, integration of heterogeneous models and rigorous composition of digital twin-enabled systems.

Abstract To enable the reuse of Digital Twins, in the form of simulation units or other forms of behavioral models, of single physical components, one must be able to connect and couple them. Current platform and architectures consider mostly monolithic digital twins and offer little support for coupling and checking the consistency of the coupling. The coupling must be internally consistent—satisfy constraints related to their co-simulation—and externally consistent—mirror the structure of the composed physical system. In this paper, we propose an extension to a behavior-extended Digital Twin architecture for individual Digital Twins to include co-simulation scenarios for coupled systems lifted from configuration files, which can be implemented along with a Digital-Twin-as-a-Service platform to make assets reusable in time. To monitor and query these connections, we introduce a semantic lifting service, which interprets the coupled Digital Twins as Knowledge Graphs and enables the use of queries to express internal and external consistency constraints. Two representative case studies for systems with coupled behavior are used for the demonstration of this approach and show that it indeed enables reusability of components and services between different Digital Twins.

Abstract Digital twin (DT) technology has been a topic with academic and industrial coverage in recent years. DTs are intended to be a virtual high‐fidelity representation of a physical counterpart. Its complex nature requires several components to create and run a DT, and that is why many DT frameworks have been proposed in the literature. There are also many surveys of DTs, but none that is bottom‐up with concrete examples and focused on open‐source software. This survey analyzes 14 open‐source DT frameworks in 10 different dimensions, which are then categorized in six different groups according to their modeling and technological domain, to present the reader different options for creating and managing DT applications, and to understand potential combinations, uses, and limitations of the tools. It also presents a case study with five of the explored DT frameworks, describing the process on how the DT is set up and comparing their capabilities based on the services to be provided by the DT. Finally, it discusses advantages and limitations of the tools according to domain, requirements, and scope, relevant aspects regarding built‐in simulations and data analytics, theory‐to‐practice transition, and advantages/disadvantages of using open‐source software instead of commercial. Main limitations of the study due to its narrow niche, conclusions, and opportunities for future research regarding the potential room for improvement in terms of out‐of‐the‐box features and services for DTs, are also shown.

Digital Twin (DT) technology has gained a great deal of attention as an enabling technology, due to the benefits it can provide in relation to monitoring, optimization, efficiency, and decision-making of their physical counterparts. Developing DTs is however not an easy task, since it relies on several modeling methods and implementation aspects. Several DT platforms have emerged recently to cover the design and development of DTs, and most of them use an object-oriented paradigm.In this paper, we propose a modeling approach for DTs in cooperative systems that provides composition and reusability. This approach extends from information modeling approaches for DTs to include the representation of behavioral models and semantic relationships through an ontology. By including the semantic layer in the modeling approach, inferences are also enabled based on semantic rules and queries.The approach is translated into an object-oriented module and used in a case study composed of two cooperative robotic arms. The demonstration shows that this approach supports the modeling and functional aspects of DT systems and increases the reusability of developed DTs through composition.