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Digital Twin with Model Driven Systems Engineering
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Digital Twin with Model Driven Systems Engineering

A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Systems Engineering".

Deadline for manuscript submissions: closed (1 January 2023) | Viewed by 20958

Special Issue Editors

Prof. Dr. Thomas Clemen

E-Mail Website
Guest Editor
Department of Computer Science, Hamburg University of Applied Sciences, Berliner Tor 7, 20099 Hamburg, Germany
Interests: Artificial Intelligence (AI); multi-agent systems; human behavior modeling; decision support; collaborative AI
Prof. Dr. Yanling Zhou

E-Mail Website
Guest Editor
Academy of Artificial Intelligence and Big Data , HeFei University, Hefei 230601, China
Interests: multicast routing algorithm; data processing; Internet of Things; RFID anti-collision algorithm
Prof. Dr. C. Donald Combs

E-Mail Website
Guest Editor
School of Health Professions, Eastern Virginia Medical School, Norfolk, VA 23501, USA
Interests: simulation; healthcare; strategy; policy
Prof. Dr. Nobuyuki Ozaki

E-Mail Website
Guest Editor
Global Research Institute for mobility in Society, Institutes of Innovation for Future Scociety, Nagoya University, Nagoya 464-8601, Japan
Interests: computer vision
Dr. Thorsten Lammers

E-Mail Website
Guest Editor
Centre for Advanced Manufacturing, Faculty of Engineering and IT, University of Technology Sydney, Broadway, NSW 2007, Australia
Interests: digital technology management; AI management; deep tech startup ecosystems; complexity management; sustainability impacts of digital technologies; smart operations and engineering management

Special Issue Information

Dear Colleagues,

Digital twins play a vital role in manufacturing, engineering, public health, logistics, smart cities, environmental management, and many other fields. The intelligent and seamless integration of data between physical and biological entities and their virtual counterparts can facilitate innovation, control, and optimization.

This Special Issue particularly looks forward to articles presenting, among others:

Fields of application and practical use cases of Digital Twins in fields such as

  • Manufacturing and Industry 4.0;
  • Urban planning and smart cities;
  • Healthcare and digital patients;
  • Robots, drones and autonomous vehicles; and
  • Logistics and supply chain management.

Enabling paradigms and technologies such as

  • Artificial intelligence;
  • Machine learning;
  • Data science;
  • The internet of things;
  • Modeling and simulation; and
  • Multiagent systems

Challenges to the development of digital twins such as

  • Safety;
  • Data privacy and security;
  • Trust and expectations; and
  • Ethics.

Prof. Dr. Thomas Clemen
Prof. Dr. Yanling Zhou
Prof. Dr. Donald Combs
Prof. Dr. Nobuyuki Ozaki
Dr. Thorsten Lammers
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Systems is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (5 papers)

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Research

20 pages, 7950 KiB  
Article
Supporting Digital Twins for the Retrofit in Aviation by a Model-Driven Data Handling
by Fabian Niklas Laukotka and Dieter Krause
Systems 2023, 11(3), 142; https://doi.org/10.3390/systems11030142 - 8 Mar 2023
Cited by 6 | Viewed by 2593
Abstract
Aviation is characterized by many stakeholders, long lifespans of its assets, and high requirements regarding safety, security, and documentation. To meet these requirements as well as customer needs, aircraft are regularly retrofitted with new cabins. During the planning and execution of this cabin [...] Read more.
Aviation is characterized by many stakeholders, long lifespans of its assets, and high requirements regarding safety, security, and documentation. To meet these requirements as well as customer needs, aircraft are regularly retrofitted with new cabins. During the planning and execution of this cabin retrofit, handling the needed and available data poses a challenge to the engineers. While much of the required data is available in some form, generally there is a lack of a digitally usable dataset of the specific aircraft—a virtual representation of the physical asset is missing. To support the implementation of such a digital twin and, thus, the overall process of retrofitting aircraft, an approach to model-driven data handling tailored to the unique circumstances and requirements of aviation is introduced. The methodology consists of a combination of systems engineering and data science techniques framed by an overarching procedure that iteratively creates and enhances a digitally accessible dataset of the relevant data, hence supporting the retrofit engineers by easing access to needed information. Besides the presentation of the research background and the methodology, a simplified example is shown, demonstrating the approach using abstracted but realistic information provided by partners from the industry. Full article
(This article belongs to the Special Issue Digital Twin with Model Driven Systems Engineering)
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14 pages, 1255 KiB  
Article
Data and Model Harmonization Research Challenges in a Nation Wide Digital Twin
by Jean-Sébastien Sottet and Cédric Pruski
Systems 2023, 11(2), 99; https://doi.org/10.3390/systems11020099 - 11 Feb 2023
Cited by 3 | Viewed by 2239
Abstract
Nation Wide Digital Twin is an emerging paradigm that pushes the context of a classical Digital Twin to a whole country. Under this perspective, models, which are central for digital twins, will play a key role for the design and implementation of such [...] Read more.
Nation Wide Digital Twin is an emerging paradigm that pushes the context of a classical Digital Twin to a whole country. Under this perspective, models, which are central for digital twins, will play a key role for the design and implementation of such a specific digital twin. However, to achieve a nation wide digital twin vision, a whole set of problems related to models have to be solved. In this paper, we detailed the notion of nation wide digital twin with respect to well known digital twin from a model point of view and discuss the problems the community is facing in this context. As a result, from the identified challenges, we propose a research road-map paving the way for future scientific contributions. Full article
(This article belongs to the Special Issue Digital Twin with Model Driven Systems Engineering)
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13 pages, 4015 KiB  
Article
On the Notion of Digital Twins: A Modeling Perspective
by Bedir Tekinerdogan
Systems 2023, 11(1), 15; https://doi.org/10.3390/systems11010015 - 30 Dec 2022
Cited by 9 | Viewed by 3827
Abstract
A digital twin is a digital replica of a physical entity that can be remotely controlled, which allows for sophisticated control for a variety of reasons. Digital twins are made possible using various technologies such as Internet of Things, sensor technology, artificial intelligence, [...] Read more.
A digital twin is a digital replica of a physical entity that can be remotely controlled, which allows for sophisticated control for a variety of reasons. Digital twins are made possible using various technologies such as Internet of Things, sensor technology, artificial intelligence, data science, and machine learning. With this, it represents a new stage in smart systems engineering. Developing digital twin-based systems necessitates a holistic system engineering approach in which modeling is critical. Various studies have been published on the notion of digital twins and its applications in various domains, but a modeling perspective has not been explicitly considered. Hence, this article provides a novel insight on the notion of digital twins from a modeling perspective, describing the evolution of modeling in engineering and likewise providing a rational basis for digital twins as a next logical step in modeling. A metamodel is provided that integrates the key concepts of systems engineering, digital twins, and modeling. While elaborating on the existing evolution of modeling in engineering, it is stated that the next step of digital twins will be artificial twins. Full article
(This article belongs to the Special Issue Digital Twin with Model Driven Systems Engineering)
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11 pages, 1400 KiB  
Article
The Digital Value Stream Twin
by Nicholas Frick and Joachim Metternich
Systems 2022, 10(4), 102; https://doi.org/10.3390/systems10040102 - 17 Jul 2022
Cited by 13 | Viewed by 3475
Abstract
The Value Stream Method (VSM) is widely used in manufacturing to analyze and redesign value streams. The aim is to improve processes, reduce waste and create a thorough product flow. Despite having many benefits, VSM also comes with disadvantages regarding modern dynamic production [...] Read more.
The Value Stream Method (VSM) is widely used in manufacturing to analyze and redesign value streams. The aim is to improve processes, reduce waste and create a thorough product flow. Despite having many benefits, VSM also comes with disadvantages regarding modern dynamic production environments. It fails to meet the requirement of providing reliable information for a realistic Value Stream Design (VSD) followed by targeted improvement activities. As a result, the VSM is usually subject to uncertainty and relies on expert knowledge. Digitalization, on the other hand, is leading to an increasing availability of production data. The use of data has the potential to support the VSM with targeted data preparation. In this regard, the concept of Digital Twin (DT) offers the capability of providing the required database to systematically collect and condense this data. This paper provides a framework for the Digital Value Stream Twin (DVST). In addition, requirements for the implementation of a DVST in practice will be elaborated. Full article
(This article belongs to the Special Issue Digital Twin with Model Driven Systems Engineering)
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18 pages, 2768 KiB  
Article
Analyzing the Implementation of a Digital Twin Manufacturing System: Using a Systems Thinking Approach
by Jonatan H. Loaiza and Robert J. Cloutier
Systems 2022, 10(2), 22; https://doi.org/10.3390/systems10020022 - 22 Feb 2022
Cited by 15 | Viewed by 6522
Abstract
Digital twin (DT) is a technology that promises great benefits for the manufacturing industry. Nevertheless, DT implementation presents many challenges. This article looks to understand and study the problems associated with the implementation of DT models in a manufacturing domain. It applies systems [...] Read more.
Digital twin (DT) is a technology that promises great benefits for the manufacturing industry. Nevertheless, DT implementation presents many challenges. This article looks to understand and study the problems associated with the implementation of DT models in a manufacturing domain. It applies systems thinking techniques to analyze and refine these problems. Systems thinking presents several methods and tools that help in studying a problem space and a solution space. The conceptagon framework describes the DT model as a system with several attributes and analyzes it in detail. A systemigram shows the relationship of manufacturing systems and the DT model. It maps the processes and components for DT implementation. The TRIZ method analyzes, and forecasts problems related to DT development and provides solutions based on patterns of invention. The CATWOE analysis allows identification of stakeholders and the study of the DT model from their perspectives. It provides a root definition of the DT model to refine a problem and the problem’s contradiction. The 9 windows tool helps to delimit the DT implementation problem, based on time and space. It gives eight more perspectives to solve the DT problem. Finally, the ideal final result (IFR) method provides the ideal DT model concept for manufacturing systems. Full article
(This article belongs to the Special Issue Digital Twin with Model Driven Systems Engineering)
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