Introduction to Digital Twin Engineering

Feng, Hao; Gomes, Cláudio; Thule, Casper; Lausdahl, Kenneth; Iosifidis, Alexandros; Larsen, Peter Gorm

doi:10.23919/annsim52504.2021.9552135

Cited by 32 publications

(10 citation statements)

References 35 publications

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“…In this section, in order to explore some of the capabilities and features of some of the tools and give the readers a notion of how these frameworks can be implemented for a particular use case, we present implementations using the Incubator case study, introduced in Reference 24, as a simple DT scenario of a heating/cooling system.…”

Section: Case Study—incubatormentioning

confidence: 99%

“…Left is the schematic overview of the incubator and right is the real incubator system. Adapted from Reference 24.…”

Section: Case Study—incubatormentioning

confidence: 99%

“…The fan is always on when the incubator is turned on, while the heatbed can be switched on or off by the controller. For more details, we refer the reader to the technical report 24 and the online documentation.…”

Section: Case Study—incubatormentioning

confidence: 99%

“…In order to perform an in‐depth survey of open‐source DT frameworks, we selected the case study proposed in Reference 23 and detailed in Reference 24, that is representative of a cyber‐physical system (CPS) for evaluating DT frameworks. We then collected 14 DT frameworks and analyzed their documentation.…”

Section: Introductionmentioning

confidence: 99%

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Survey on open‐source digital twin frameworks–A case study approach

Gil,

Mikkelsen,

Gomes

et al. 2024

Softw Pract Exp

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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.

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Section: Case Study—incubatormentioning

confidence: 99%

“…Left is the schematic overview of the incubator and right is the real incubator system. Adapted from Reference 24.…”

Section: Case Study—incubatormentioning

confidence: 99%

Section: Case Study—incubatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Survey on open‐source digital twin frameworks–A case study approach

Gil,

Mikkelsen,

Gomes

et al. 2024

Softw Pract Exp

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“…Different to a cycle-accurate simulator, a Digital Twin often focuses on a higher level of abstraction where the key characteristics are identified. In general, the proposed DT is designed to meet the following required purposes: (1) answering what-ifs (decision making and optimisation) [7];…”

Section: Design Philosophymentioning

confidence: 99%

Using Digital Twins in the Development of Complex Dependable Real-Time Embedded Systems

Dai¹,

Zhao²,

Lesage³

et al. 2022

Lecture Notes in Computer Science

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Modelling execution times in complex real-time embedded systems is vital for understanding and predicting tasks' temporal behaviour, and to improve the system scheduling performance. Previous research mainly relied on worst-case execution time estimations based on formal static analyses that are often pessimistic. The models that resulted are hard to maintain and even harder to validate. In this work, the novel use of Digital Twins provides opportunities to improve this issue and beyond for dependable real-time systems. We aim to establish and contribute to three questions: (i) how to easily model execution times with an adequate level of abstraction, and how to evaluate the quality of that model; (ii) how to identify errors in the models and how to evaluate the impact of errors; and (iii) how to make decisions as to when and how to improve the models. In this paper, we proposed a Digital Twin-based adaptation framework, and demonstrated its use for modelling and refining execution time profiles. Key decisions concerning the quality of the model and its impact on performance are evaluated. Finally, some challenges and key research questions for the formal method community are proposed.

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