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Model-Based Systems Engineering: Rigorous Foundations for Digital Transformations in Science and Engineering

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 January 2021) | Viewed by 43316

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Special Issue Editors

Prof. Dov Dori

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Guest Editor

1. Faculty of Industrial Engineering and Management, Technion—Israel Institute of Technology, Haifa, Israel
2. Department of Aeronautics and Astronautics, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
Interests: conceptual modelling; object–process methodology; model-based systems engineering; systems and software engineering; Industry 4.0; complex systems; Internet of Things; Internet of Robotic Things; cyber-physical systems; model-based systems biology

Dr. Yaniv Mordecai

E-Mail Website
Guest Editor

Engineering Systems Lab, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
Interests: model-based systems engineering; cyber-physical informatics; digital systems engineering; model informatics and analytics; modelling and simulation of cyber-physical systems; command and control; enterprise architectures

Special Issue Information

Dear Colleagues,

Over the last decade, recognition is growing that models are essential as formal representations of complex systems for transforming systems engineering (SE) in a rigorous and robust meta-engineering discipline, positioning the model-based systems engineering (MBSE) paradigm as the leading SE approach. Although enterprises and organizations of all kinds and sizes have started adopting MBSE, emerging information and communication technologies, such as the Internet of Things (IoT) and the Internet of Robotic Things (IoRT), are shaping Industry 4.0, the fourth industrial revolution. This is only one of the expressions of the digital transformation we are witnessing in a variety of traditional ecosystems, from product conception, design, development, and service delivery, through supply chains and manufacturing, to customer relationship management and social networking. These emerging technologies have been challenging traditional business and operations practices; they have also begun to pose new challenges for the MBSE paradigm, which, in addition to its previous role, must now adopt and adapt to the digital transformation. Leading paradigms for complex systems modelling, analysis, and simulation, including the systems modelling language (SysML), the unified modelling language (UML), or the object process methodology (OPM), must overcome new challenges, such as the modelling of cyber-physical interactions, the digital twin revolution, continuous integration and innovation, and high edge computing workloads supported by cloud-based infrastructure. To meet these challenges, novel approaches for modelling and executable simulation of conceptual models are needed. These include rich and evolving requirements modelling; joint conceptual–computational modelling and execution; model-in-the-loop frameworks; multi-model orchestration; model-based extended (MBX) approaches that rely on the system models for tasks such as risk management, testing, and verification; product and service cataloguing; maintenance and operations; and model analytics to drive stakeholder decisions. Given these developments, enablers, such as model-based teamwork, management commitment, stakeholder engagement, model curation, and model reuse, require fresh insight. Educating and training systems engineers that will be ready to cope with the digital systems engineering challenges, primarily Industry 4.0, need to be addressed as well. The recent COVID-19 pandemic is imposing even greater and historically unprecedented disruptions, transformations, and challenges in all avenues of human life, including healthcare, supply chains, manufacturing, commerce, energy, services, and socialization. MBSE can and should be instrumental in creating a smoother transformation to a new reality that will likely change how we live and operate in years to come.

We invite you to submit original research papers on these and related topics, some of which are listed in the keywords below.

Prof. Dov Dori
Dr. Yaniv Mordecai
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. Applied Sciences is an international peer-reviewed open access semimonthly 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.

Keywords

Modelling
Model-based systems engineering (MBSE)
Modelling languages and constructs for Industry 4.0
Digital systems engineering
Digital transformation
Digital twin
Smart factory, smart manufacturing
Internet of Things (IoT)/Industrial Internet of Things (IIoT)
Internet of Robotic Things (IoRT)
Cyber-physical systems
Cyber-physical informatics
Model-based requirements
Model-based testing
MBSE training and education
Model-based extended approaches (MBX): MB systems thinking, MB tradespace exploration, MB risk analysis, MB compliance assurance, MB diagnosis, MB operational planning, MB lifecycle support, etc.
Model simulation and execution
Model informatics and analytics
Model-based application development and code generation
Combining conceptual modeling with other SE techniques, such as systems dynamics, design structure matrix, axiomatic design
Comparative studies in MBSE
MBSE adoption, cost-benefit analysis, and return-on-investment case studies
Model-based collaboration, teamwork, knowledge management, and group communication
Innovative applications of MBSE, such as for digital enterprise architectures, evolving systems, intelligent transportation systems, mobilization of healthcare systems, and coping with COVID 19
Model-Based Systems Science
Modeling Applied Science Research Systems

Published Papers (14 papers)

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Research

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25 pages, 5287 KiB

Open AccessArticle

Conjoining Wymore’s Systems Theoretic Framework and the DEVS Modeling Formalism: Toward Scientific Foundations for MBSE

by Paul Wach, Bernard P. Zeigler and Alejandro Salado

Appl. Sci. 2021, 11(11), 4936; https://doi.org/10.3390/app11114936 - 27 May 2021

Cited by 9 | Viewed by 2531

Abstract

The objective of this research article is to re-introduce some of the concepts provided by A. Wayne Wymore in his mathematical theory of Model-Based Systems Engineering, discuss why his framework might have not been adopted, and define a potential path to modernize the framework for practical application in the digital age. The dense mathematical theory has never been converted to a practical form. We propose a path to modernization by creating a metamodel of Wymore’s mathematical theory of MBSE. This enables explaining the concepts in simple to understand terms and shows the internal consistency provided by the theory. Furthermore, the metamodel allows for conversion of the theory into software application, for which we show some initial results that open the research to the art of the possible. In recognition of limitation of the theory, we make the case for a merger of the theoretical framework with the enhanced formalism of Discrete Event System Specification (DEVS). This will establish a path toward the scientific foundations for MBSE to enable future implementations of the complementary pairing and their empirical results. Full article

(This article belongs to the Special Issue Model-Based Systems Engineering: Rigorous Foundations for Digital Transformations in Science and Engineering)

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24 pages, 3091 KiB

Open AccessArticle

Stochastic Model Driven Performance and Availability Planning for a Mobile Edge Computing System

by Carlos Brito, Laécio Rodrigues, Brena Santos, Iure Fé, Tuan-Anh Nguyen, Dugki Min, Jae-Woo Lee and Francisco Airton Silva

Appl. Sci. 2021, 11(9), 4088; https://doi.org/10.3390/app11094088 - 29 Apr 2021

Cited by 2 | Viewed by 1882

Abstract

Mobile Edge Computing (MEC) has emerged as a promising network computing paradigm associated with mobile devices at local areas to diminish network latency under the employment and utilization of cloud/edge computing resources. In that context, MEC solutions are required to dynamically allocate mobile requests as close as possible to their computing resources. Moreover, the computing power and resource capacity of MEC server machines can directly impact the performance and operational availability of mobile apps and services. The systems practitioners must understand the trade off between performance and availability in systems design stages. The analytical models are suited to such an objective. Therefore, this paper proposes Stochastic Petri Net (SPN) models to evaluate both performance and availability of MEC environments. Different to previous work, our proposal includes unique metrics such as discard probability and a sensitivity analysis that guides the evaluation decisions. The models are highly flexible by considering fourteen transitions at the base model and twenty-five transitions at the extended model. The performance model was validated with a real experiment, the result of which indicated equality between experiment and model with p-value equal to 0.684 by t-Test. Regarding availability, the results of the extended model, different from the base model, always remain above 99%, since it presents redundancy in the components that were impacting availability in the base model. A numerical analysis is performed in a comprehensive manner, and the output results of this study can serve as a practical guide in designing MEC computing system architectures by making it possible to evaluate the trade-off between Mean Response Time (MRT) and resource utilization. Full article

(This article belongs to the Special Issue Model-Based Systems Engineering: Rigorous Foundations for Digital Transformations in Science and Engineering)

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34 pages, 40942 KiB

Open AccessArticle

Learn-CIAM: A Model-Driven Approach for the Development of Collaborative Learning Tools

by Yoel Arroyo, Ana I. Molina, Miguel A. Redondo and Jesús Gallardo

Appl. Sci. 2021, 11(6), 2554; https://doi.org/10.3390/app11062554 - 12 Mar 2021

Cited by 3 | Viewed by 1734

Abstract

This paper introduces Learn-CIAM, a new model-based methodological approach for the design of flows and for the semi-automatic generation of tools in order to support collaborative learning tasks. The main objective of this work is to help professors by establishing a series of steps for the specification of their learning courses and the obtaining of collaborative tools to support certain learning activities (in particular, for in-group editing, searching and modeling). This paper presents a complete methodological framework, how it is supported conceptually and technologically, and an application example. So to guarantee the validity of the proposal, we also present some validation processes with potential designers and users from different profiles such as Education and Computer Science. The results seem to demonstrate a positive reception and acceptance, concluding that its application would facilitate the design of learning courses and the generation of collaborative learning tools for professionals of both profiles. Full article

(This article belongs to the Special Issue Model-Based Systems Engineering: Rigorous Foundations for Digital Transformations in Science and Engineering)

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20 pages, 42990 KiB

Open AccessArticle

MBSE Testbed for Rapid, Cost-Effective Prototyping and Evaluation of System Modeling Approaches

by Azad M. Madni

Appl. Sci. 2021, 11(5), 2321; https://doi.org/10.3390/app11052321 - 05 Mar 2021

Cited by 4 | Viewed by 3222

Abstract

Model-based systems engineering (MBSE) has made significant strides in the last decade and is now beginning to increase coverage of the system life cycle and in the process generating many more digital artifacts. The MBSE community today recognizes the need for a flexible framework to efficiently organize, access, and manage MBSE artifacts; create and use digital twins for verification and validation; facilitate comparative evaluation of system models and algorithms; and assess system performance. This paper presents progress to date in developing a MBSE experimentation testbed that addresses these requirements. The current testbed comprises several components, including a scenario builder, a smart dashboard, a repository of system models and scenarios, connectors, optimization and learning algorithms, and simulation engines, all connected to a private cloud. The testbed has been successfully employed in developing an aircraft perimeter security system and an adaptive planning and decision-making system for autonomous vehicles. The testbed supports experimentation with simulated and physical sensors and with digital twins for verifying system behavior. A simulation-driven smart dashboard is used to visualize and conduct comparative evaluation of autonomous and human-in-the-loop control concepts and architectures. Key findings and lessons learned are presented along with a discussion of future directions. Full article

(This article belongs to the Special Issue Model-Based Systems Engineering: Rigorous Foundations for Digital Transformations in Science and Engineering)

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27 pages, 8504 KiB

Open AccessArticle

Model-Driven Design and Development of Flexible Automated Production Control Configurations for Industry 4.0

by Unai Gangoiti, Alejandro López, Aintzane Armentia, Elisabet Estévez and Marga Marcos

Appl. Sci. 2021, 11(5), 2319; https://doi.org/10.3390/app11052319 - 05 Mar 2021

Cited by 11 | Viewed by 2877

Abstract

The continuous changes of the market and customer demands have forced modern automation systems to provide stricter Quality of service (QoS) requirements. This work is centered in automation production system flexibility, understood as the ability to shift from one controller configuration to a different one, in the most quick and cost-effective way, without disrupting its normal operation. In the manufacturing field, this allows to deal with non-functional requirements such as assuring control system availability or workload balancing, even in the case of failure of a machine, components, network or controllers. Concretely, this work focuses on flexible applications at production level, using Programmable Logic Controllers (PLCs) as primary controllers. The reconfiguration of the control system is not always possible as it depends on the process state. Thus, an analysis of the system state is necessary to make a decision. In this sense, architectures based on industrial Multi Agent Systems (MAS) have been used to provide this support at runtime. Additionally, the introduction of these mechanisms makes the design and the implementation of the control system more complex. This work aims at supporting the design and development of such flexible automation production systems, through the proposed model-based framework. The framework consists of a set of tools that, based on models, automate the generation of control code extensions that add flexibility to the automation production system, according to industry 4.0 paradigm. Full article

(This article belongs to the Special Issue Model-Based Systems Engineering: Rigorous Foundations for Digital Transformations in Science and Engineering)

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24 pages, 14548 KiB

Open AccessArticle

A Conceptual Model for Joint Graphic Representation of Mechatronic Systems with Servomechanisms

by Julio Garrido, David Santos, Diego Silva, Enrique Riveiro and Juan Sáez

Appl. Sci. 2021, 11(5), 2310; https://doi.org/10.3390/app11052310 - 05 Mar 2021

Viewed by 2335

Abstract

This article deals with the problem of joint representation of mechanical and motion control information of machines with servo axes. A new conceptual model is proposed for the graphical representation of industrial mechatronic systems covering the minimum information requirements from both mechanical and motion automation points of view. The model also takes into account new electronic motion control concepts such as virtual axes and temporary electronic coordination relationships between axes (e-gears). The objective is to support more integrated and collaborative work between mechanical designers and automation developers when implementing complex machines and industrial mechatronic systems. Schemes graphically representing the relevant common information are obtained from the information model, which may simplify the exchange of information between the mechanical and the motion control fields, not only at conceptualization and design stages, but also throughout the rest of the implementation process of industrial mechatronic systems. Full article

(This article belongs to the Special Issue Model-Based Systems Engineering: Rigorous Foundations for Digital Transformations in Science and Engineering)

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23 pages, 8279 KiB

Open AccessArticle

Improving Conceptual Modeling with Object-Process Methodology Stereotypes

by Hanan Kohen and Dov Dori

Appl. Sci. 2021, 11(5), 2301; https://doi.org/10.3390/app11052301 - 05 Mar 2021

Cited by 3 | Viewed by 3863

Abstract

As system complexity is on the rise, there is a growing need for standardized building blocks to increase the likelihood of systems’ success. Conceptual modeling is the primary activity required for engineering systems to be understood, designed, and managed. Modern modeling languages enable describing the requirements and design of systems in a formal yet understandable way. These languages use stereotypes to standardize, clarify the model semantics, and extend the meaning of model elements. An Internet of things (IoT) system serves as an example to show the significant contributions of stereotypes to model construction, comprehension, error reduction, and increased productivity during design, simulation, and combined hardware–software system execution. This research emphasizes stereotype features that are unique to Object-Process Methodology (OPM) ISO 19450, differentiating it from stereotypes in other conceptual modeling languages. We present the implementation of stereotypes in OPCloud, an OPM modeling software environment, explore stereotype-related problems, propose solutions, and discuss future enhancements. Full article

(This article belongs to the Special Issue Model-Based Systems Engineering: Rigorous Foundations for Digital Transformations in Science and Engineering)

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30 pages, 4856 KiB

Open AccessArticle

Category-Theoretic Formulation of the Model-Based Systems Architecting Cognitive-Computational Cycle

by Yaniv Mordecai, James P. Fairbanks and Edward F. Crawley

Appl. Sci. 2021, 11(4), 1945; https://doi.org/10.3390/app11041945 - 23 Feb 2021

Cited by 13 | Viewed by 3937

Abstract

We introduce the Concept→Model→Graph→View Cycle (CMGVC). The CMGVC facilitates coherent architecture analysis, reasoning, insight, and decision making based on conceptual models that are transformed into a generic, robust graph data structure (GDS). The GDS is then transformed into multiple views of the model, which inform stakeholders in various ways. This GDS-based approach decouples the view from the model and constitutes a powerful enhancement of model-based systems engineering (MBSE). The CMGVC applies the rigorous foundations of Category Theory, a mathematical framework of representations and transformations. We show that modeling languages are categories, drawing an analogy to programming languages. The CMGVC architecture is superior to direct transformations and language-coupled common representations. We demonstrate the CMGVC to transform a conceptual system architecture model built with the Object Process Modeling Language (OPM) into dual graphs and a stakeholder-informing matrix that stimulates system architecture insight. Full article

(This article belongs to the Special Issue Model-Based Systems Engineering: Rigorous Foundations for Digital Transformations in Science and Engineering)

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19 pages, 3617 KiB

Open AccessFeature PaperArticle

Using Domain-Specific Models to Facilitate Model-Based Systems-Engineering: Development Process Design Modeling with OPM and PROVE

by Avi Shaked and Yoram Reich

Appl. Sci. 2021, 11(4), 1532; https://doi.org/10.3390/app11041532 - 08 Feb 2021

Cited by 10 | Viewed by 3243

Abstract

Model-based Systems Engineering (MBSE) approaches are a step forward in the evolution of computer-aided engineering, and yet, they often incorporate deficiencies that may jeopardize their practical utility and usability, as well as the validity of the resulting models. We demonstrate how a domain-specific modeling approach can relieve some hurdles in adopting MBSE, and how it can be used in tandem with a general-purpose modeling approach to augment and introduce rigor to models. Specifically, we demonstrate the consequences of theoretical issues that were previously identified in Object Process Methodology and suggest an approach to solve them. We use a generalized case-study—derived from extensive process modeling in both academia and industry—to show that a domain-specific model can significantly relax the user’s modeling effort. This demonstration is based on two quantitative metrics: the number of representational elements and available modeling tactics. We discuss the contribution of our approach to model quality, particularly with respect to its rigor and communicability. Full article

(This article belongs to the Special Issue Model-Based Systems Engineering: Rigorous Foundations for Digital Transformations in Science and Engineering)

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18 pages, 6587 KiB

Open AccessArticle

Quantitative Set-Based Design to Inform Design Teams

by Eric Specking, Nicholas Shallcross, Gregory S. Parnell and Edward Pohl

Appl. Sci. 2021, 11(3), 1239; https://doi.org/10.3390/app11031239 - 29 Jan 2021

Cited by 4 | Viewed by 1969

Abstract

System designers, analysts, and engineers use various techniques to develop complex systems. A traditional design approach, point-based design (PBD), uses system decomposition and modeling, simulation, optimization, and analysis to find and compare discrete design alternatives. Set-based design (SBD) is a concurrent engineering technique that compares a large number of design alternatives grouped into sets. The existing SBD literature discusses the qualitative team-based characteristics of SBD, but lacks insights into how to quantitatively perform SBD in a team environment. This paper proposes a qualitative SBD conceptual framework for system design, proposes a team-based, quantitative SBD approach for early system design and analysis, and uses an unmanned aerial vehicle case study with an integrated model-based engineering framework to demonstrate the potential benefits of SBD. We found that quantitative SBD tradespace exploration can identify potential designs, assess design feasibility, inform system requirement analysis, and evaluate feasible designs. Additionally, SBD helps designers and analysts assess design decisions by providing an understanding of how each design decision affects the feasible design space. We conclude that SBD provides a more holistic tradespace exploration process since it provides an integrated examination of system requirements and design decisions. Full article

(This article belongs to the Special Issue Model-Based Systems Engineering: Rigorous Foundations for Digital Transformations in Science and Engineering)

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29 pages, 11691 KiB

Open AccessArticle

Gaining Insights into Conceptual Models: A Graph-Theoretic Querying Approach

by Danny Medvedev, Uri Shani and Dov Dori

Appl. Sci. 2021, 11(2), 765; https://doi.org/10.3390/app11020765 - 14 Jan 2021

Cited by 12 | Viewed by 2837

Abstract

Modern complex systems include products and services that comprise many interconnected pieces of integrated hardware and software, which are expected to serve humans interacting with them. As technology advances, expectations of a smooth, flawless system operation grow. Model-based systems engineering, an approach based on conceptual models, copes with this challenge. Models help construct formal system representations, visualize them, understand the design, simulate the system, and discover design flaws early on. Modeling tools can benefit tremendously from querying capabilities that enable gaining deep insights into system aspects that direct model observations do not reveal. Querying mechanisms can unveil and explain cause-and-effect phenomena, identify central components, and estimate impacts or risks associated with changes. Being connected networks of system elements, models can be effectively represented as graphs, to which queries are applied. Capitalizing on established graph-theoretic algorithms to solve a large variety of problems can elevate the modeling experience to new levels. To utilize this rich set of capabilities, one must convert the model into a graph and store it in a graph database with no significant loss of information. Applying the appropriate algorithms and translating the query response back to the original intelligible and meaningful diagrammatic and textual model representation is most valuable. We present and demonstrate a querying approach of converting Object-Process Methodology (OPM) ISO 19450 models into graphs, storing them in a Neo4J graph database, and performing queries that answer complex questions on various system aspects, providing key insights into the modeled system or phenomenon and helping to improve the system design. Full article

(This article belongs to the Special Issue Model-Based Systems Engineering: Rigorous Foundations for Digital Transformations in Science and Engineering)

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20 pages, 4776 KiB

Open AccessArticle

A MBSE Application to Controllers of Autonomous Underwater Vehicles Based on Model-Driven Architecture Concepts

by Ngo Van Hien, Ngo Van He, Van-Thuan Truong and Ngoc-Tam Bui

Appl. Sci. 2020, 10(22), 8293; https://doi.org/10.3390/app10228293 - 23 Nov 2020

Cited by 4 | Viewed by 3463

Abstract

In this paper, a hybrid realization model is proposed for the controllers of autonomous underwater vehicles (AUVs). This model is based on the model-based systems engineering (MBSE) methodology, in combination with the model-driven architecture (MDA), the real-time unified modeling language (UML)/systems modeling language (SysML), the extended/unscented Kalman filter (EKF/UKF) algorithms, and hybrid automata, and it can be reused for designing controllers of various AUV types. The dynamic model and control structure of AUVs were combined with the specialization of MDA concepts as follows. The computation-independent model (CIM) was specified by the use-case model combined with the EKF/UKF algorithms and hybrid automata to intensively gather the control requirements. Then, the platform-independent model (PIM) was specialized using the real-time UML/SysML to design the capsule collaboration of control and its connections. The detailed PIM was subsequently converted into the platform-specific model (PSM) using open-source platforms to promptly realize the AUV controller. On the basis of the proposed hybrid model, a planar trajectory-tracking controller, which allows a miniature torpedo-shaped AUV to autonomously track the desired planar trajectory, was implemented and evaluated, and shown to have good feasibility. Full article

(This article belongs to the Special Issue Model-Based Systems Engineering: Rigorous Foundations for Digital Transformations in Science and Engineering)

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37 pages, 3786 KiB

Open AccessArticle

Extending Drag-and-Drop Actions-Based Model-to-Model Transformations with Natural Language Processing

by Paulius Danenas, Tomas Skersys and Rimantas Butleris

Appl. Sci. 2020, 10(19), 6835; https://doi.org/10.3390/app10196835 - 29 Sep 2020

Cited by 6 | Viewed by 2730

Abstract

Model-to-model (M2M) transformations are among the key components of model-driven development, enabling a certain level of automation in the process of developing models. The developed solution of using drag-and-drop actions-based M2M transformations contributes to this purpose by providing a flexible, reusable, customizable, and relatively easy-to-use transformation method and tool support. The solution uses model-based transformation specifications triggered by user-initiated drag-and-drop actions within the model deployed in a computer-aided software engineering (CASE) tool environment. The transformations are called partial M2M transformations, meaning that a specific user-defined fragment of the source model is being transformed into a specific fragment of the target model and not running the whole model-level transformation. In this paper, in particular, we present the main aspects of the developed extension to that M2M transformation method, delivering a set of natural language processing (NLP) techniques on both the conceptual and implementation level. The paper addresses relevant developments and topics in the field of natural language processing and presents a set of operators that can be used to satisfy the needs of advanced textual preprocessing in the scope of M2M transformations. Also in this paper, we describe the extensions to the previous M2M transformation metamodel necessary for enabling the solution’s NLP-related capabilities. The usability and actual benefits of the proposed extension are introduced by presenting a set of specific partial M2M transformation use cases where natural language processing provides actual solutions to previously unsolvable situations when using the previous M2M transformation development. Full article

(This article belongs to the Special Issue Model-Based Systems Engineering: Rigorous Foundations for Digital Transformations in Science and Engineering)

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Review

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34 pages, 859 KiB

Open AccessReview

Systematic Literature Review of System Models for Technical System Development

by Marvin M. Schmidt, Thomas C. Zimmermann and Rainer Stark

Appl. Sci. 2021, 11(7), 3014; https://doi.org/10.3390/app11073014 - 28 Mar 2021

Cited by 9 | Viewed by 3478

Abstract

In Model-Based Systems Engineering (MBSE) there is yet no converged terminology. The term ‘system model’ is used in different contexts in literature. In this study we elaborated the definitions and usages of the term ‘system model’, to find a common definition. We analyzed 104 publications in depth for their usage and definition as well as their meta-data e.g., the publication year and publication background to find some common patterns. While the term is gaining more interest in recent years, it is used in a broad range of contexts for both analytical and synthetic use cases. Based on this, three categories of system models have been defined and integrated into a more precise definition. Full article

(This article belongs to the Special Issue Model-Based Systems Engineering: Rigorous Foundations for Digital Transformations in Science and Engineering)

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