Special Issue "Model-Based Systems Engineering"

A special issue of Systems (ISSN 2079-8954).

Deadline for manuscript submissions: closed (31 January 2019)

Special Issue Editors

Guest Editor
Prof. Dr. Azad M Madni

1. Executive Director, Systems Architecting and Engineering Program, University of Southern California, Los Angeles, CA 90089, USA
2. Professor, Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089, USA
3. Professor, Keck School of Medicine and Rossier School of Education, University of Southern California, Los Angeles, CA 90089, USA
Website | E-Mail
Interests: cross-disciplinary approaches to complex systems engineering; engineered resilient systems; formal methods for systems and system-of-systems engineering; model based systems engineering; cyber–physical–human systems; machine learning; human–technology integration
Guest Editor
Dr. Mike Sievers

Jet Propulsion Laboratory, University of Southern California, Los Angeles, CA 90089, USA
Website | E-Mail
Interests: model based systems engineering; fault-tolerant systems; system verification and testing; formal methods in systems integration and system-of-systems integration; resilience engineering

Special Issue Information

Dear Colleagues,

Model-Based Systems Engineering (MBSE) has been a major advance in systems engineering over the last decade. Models created with MBSE methods can bridge disciplines, facilitate collaboration, enable cost-effective verification and testing, and support rapid exploration of system behaviors in simulation environments, thereby reducing the scope and cost of physical testing. Importantly, MBSE can accommodate a variety of methodologies and modeling constructs. However, MBSE is still evolving in terms of its coverage of the system life cycle, as well as modeling, analysis, testing and integration methods. This Special Issue is focused on presenting these advances to both the MBSE community as well as MBSE researchers and practitioners in various industries. Papers are being sought in the following areas:

  • MBSE research and practical applications
  • System modeling methods (multiple modeling constructs, hybrid models)
  • Model verification and formal proofs of correctness
  • Model visualization and simulation
  • Trade studies and reasoning with models
  • Representing time semantics
  • Modeling system resiliency and cyber-security
  • Modeling socio-technical systems
  • Modeling autonomous systems
  • Models for teaching and training
  • Incorporating decision theory and optimization in MBSE
  • Integration of cyber, physical, and human elements
  • Integration of MBSE models and methods with simulations
  • Integration of MBSE models and methods with third-party tools.

Prof. Dr. Azad M Madni
Dr. Mike Sievers
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 papers will be 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 quarterly 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 350 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.

Published Papers (10 papers)

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Open AccessArticle A Preliminary Design-Phase Security Methodology for Cyber–Physical Systems
Received: 1 February 2019 / Revised: 28 March 2019 / Accepted: 1 April 2019 / Published: 4 April 2019
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Abstract
Despite “cyber” being in the name, cyber–physical systems possess unique characteristics that limit the applicability and suitability of traditional cybersecurity techniques and strategies. Furthermore, vulnerabilities to cyber–physical systems can have significant safety implications. The physical and cyber interactions inherent in these systems require [...] Read more.
Despite “cyber” being in the name, cyber–physical systems possess unique characteristics that limit the applicability and suitability of traditional cybersecurity techniques and strategies. Furthermore, vulnerabilities to cyber–physical systems can have significant safety implications. The physical and cyber interactions inherent in these systems require that cyber vulnerabilities not only be defended against or prevented, but that the system also be resilient in the face of successful attacks. Given the complex nature of cyber–physical systems, the identification and evaluation of appropriate defense and resiliency strategies must be handled in a targeted and systematic manner. Specifically, what resiliency strategies are appropriate for a given system, where, and which should be implemented given time and/or budget constraints? This paper presents two methodologies: (1) the cyber security requirements methodology and (2) a systems-theoretic, model-based methodology for identifying and prioritizing appropriate resiliency strategies for implementation in a given system and mission. This methodology is demonstrated using a case study based on a hypothetical weapon system. An assessment and comparison of the results from the two methodologies suggest that the techniques presented in this paper can augment and enhance existing systems engineering approaches with model-based evidence. Full article
(This article belongs to the Special Issue Model-Based Systems Engineering)
Open AccessArticle Constructing True Model-Based Requirements in SysML
Received: 5 February 2019 / Revised: 11 March 2019 / Accepted: 26 March 2019 / Published: 28 March 2019
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Abstract
Some authors suggest that transitioning requirements engineering from the traditional statements in natural language with shall clauses to model-based requirements within a Model-Based Systems Engineering (MBSE) environment could improve communication, requirements traceability, and system decomposition, among others. Requirement elements in the Systems Modeling [...] Read more.
Some authors suggest that transitioning requirements engineering from the traditional statements in natural language with shall clauses to model-based requirements within a Model-Based Systems Engineering (MBSE) environment could improve communication, requirements traceability, and system decomposition, among others. Requirement elements in the Systems Modeling Language (SysML) fail to fulfill this objective, as they are really a textual requirement in natural language as a model element. Current efforts to directly leverage behavioral and structural models of the system lack an overarching theoretical framework with which to assess the adequacy of how those models are used to capture requirements. This paper presents an approach to construct true model-based requirements in SysML. The presented approach leverages some of SysML’s behavioral and structural models and diagrams, with specific construction rules derived from Wymore’s mathematical framework for MBSE and taxonomies of requirements and interfaces. The central proposition of the approach is that every requirement can be modeled as an input/output transformation. Examples are used to show how attributes traditionally thought of as non-functional requirements can be captured, with higher precision, as functional transformations. Full article
(This article belongs to the Special Issue Model-Based Systems Engineering)
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Open AccessArticle Economic Analysis of Model-Based Systems Engineering
Received: 26 December 2018 / Revised: 14 February 2019 / Accepted: 15 February 2019 / Published: 20 February 2019
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Abstract
In the face of ever-increasing complexity of systems and system development programs, several aerospace, automotive, and defense organizations have already begun or are contemplating the transition to model-based systems engineering (MBSE). The key challenges that organizations face in making this decision are determining [...] Read more.
In the face of ever-increasing complexity of systems and system development programs, several aerospace, automotive, and defense organizations have already begun or are contemplating the transition to model-based systems engineering (MBSE). The key challenges that organizations face in making this decision are determining whether it is technically feasible and financially beneficial in the long-run to transition to MBSE, and whether such transition is achievable given budgetary constraints. Among other cost drivers of this transition, are a new digital infrastructure, personnel training in MBSE, and cost-effective migration of legacy models and data into the new infrastructure. The ability to quantify gains from MBSE investment is critical to making the decision to commit to MBSE implementation. This paper proposes a methodological framework for analyzing investments and potential gains associated with MBSE implementation on large-scale system programs. To this end, the MBSE implementation problem is characterized in terms of: system complexity, environment complexity and regulatory constraints, and system lifespan. These criteria are applied to systems in twelve major industry sectors to determine MBSE investment and expected gains. Results from this cost-benefit analysis are used to justify investment in MBSE implementation where warranted. This approach is generic and can be applied to different sectors for economic evaluation of costs and benefits and justification of transition to MBSE if warranted. Full article
(This article belongs to the Special Issue Model-Based Systems Engineering)
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Open AccessArticle Model-Based Approach to Engineering Resilience in Multi-UAV Systems
Received: 26 November 2018 / Revised: 3 February 2019 / Accepted: 12 February 2019 / Published: 15 February 2019
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Abstract
Multi-UAV Operations are an area of great interest in government, industry, and research community. In multi-UAV operations, a group of unmanned aerial vehicles (UAVs) are deployed to carry out missions such as search and rescue or disaster relief. As multi-UAV systems operate in [...] Read more.
Multi-UAV Operations are an area of great interest in government, industry, and research community. In multi-UAV operations, a group of unmanned aerial vehicles (UAVs) are deployed to carry out missions such as search and rescue or disaster relief. As multi-UAV systems operate in an open operational environment, many disrupting events can occur. To this end, resilience of these systems is of great importance. The research performed and reported in this paper utilizes simulation-based research methodology and demonstrates that resilience of multi-UAV systems can be achieved by real-time evaluation of resilience alternatives during system operation. This evaluation is done using a dynamic utility function where priorities change as a function of context. Simulation results show that resilience response can in fact change depending on the context. Full article
(This article belongs to the Special Issue Model-Based Systems Engineering)
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Open AccessArticle Leveraging Digital Twin Technology in Model-Based Systems Engineering
Received: 6 November 2018 / Revised: 22 December 2018 / Accepted: 22 January 2019 / Published: 30 January 2019
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Abstract
Digital twin, a concept introduced in 2002, is becoming increasingly relevant to systems engineering and, more specifically, to model-based system engineering (MBSE). A digital twin, like a virtual prototype, is a dynamic digital representation of a physical system. However, unlike a virtual prototype, [...] Read more.
Digital twin, a concept introduced in 2002, is becoming increasingly relevant to systems engineering and, more specifically, to model-based system engineering (MBSE). A digital twin, like a virtual prototype, is a dynamic digital representation of a physical system. However, unlike a virtual prototype, a digital twin is a virtual instance of a physical system (twin) that is continually updated with the latter’s performance, maintenance, and health status data throughout the physical system’s life cycle. This paper presents an overall vision and rationale for incorporating digital twin technology into MBSE. The paper discusses the benefits of integrating digital twins with system simulation and Internet of Things (IoT) in support of MBSE and provides specific examples of the use and benefits of digital twin technology in different industries. It concludes with a recommendation to make digital twin technology an integral part of MBSE methodology and experimentation testbeds. Full article
(This article belongs to the Special Issue Model-Based Systems Engineering)
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Open AccessArticle Toward an Interoperability and Integration Framework to Enable Digital Thread
Received: 1 November 2018 / Revised: 10 December 2018 / Accepted: 12 December 2018 / Published: 18 December 2018
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Abstract
This article discusses ongoing research investigating the feasibility of supporting an interoperability and integration framework to enable the digital thread, or an authoritative source of truth with current technology. The question that initiated this exploratory research was, “Is there current technology that can [...] Read more.
This article discusses ongoing research investigating the feasibility of supporting an interoperability and integration framework to enable the digital thread, or an authoritative source of truth with current technology. The question that initiated this exploratory research was, “Is there current technology that can enable cross-domain digital artifact data sharing needed for the digital thread?” A thorough review and investigation of current state-of-the-art model-based systems engineering was performed by reviewing literature and performing multiple site visits and interviews with organizations at the forefront of digital engineering. After this initial investigation and review, a Semantic Web-enabled framework that would allow data in the thread to be captured, stored, transferred, checked for completeness and consistency, and changed under revision change control management began to be formed. This framework has gone through revisions. This paper reflects the most current demonstration of the framework and its capability of acquiring digital data, and parsing and querying the data using Semantic Web technology to generate a decision table that allows the decision data to be visualized. The article concludes with future demonstrations of the framework to further advance toward a framework that can enable a digital thread in practice. Full article
(This article belongs to the Special Issue Model-Based Systems Engineering)
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Open AccessArticle Early Design Space Exploration with Model-Based System Engineering and Set-Based Design
Received: 31 October 2018 / Revised: 10 December 2018 / Accepted: 12 December 2018 / Published: 17 December 2018
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Abstract
Adequately exploring the tradespace in the early system design phase is important to determine the best design concepts to pursue in the next life cycle stage. Tradespace exploration (TSE) often uses trade-off analysis. Set-based design (SBD) methods, compared to traditional point-based design, explore [...] Read more.
Adequately exploring the tradespace in the early system design phase is important to determine the best design concepts to pursue in the next life cycle stage. Tradespace exploration (TSE) often uses trade-off analysis. Set-based design (SBD) methods, compared to traditional point-based design, explore significantly more designs. An integrated framework with model-based system engineering (MBSE) and a life cycle cost model enables design evaluation in near real-time. This study proposes an early design phase SBD methodology and demonstrates how SBD enabled by an integrated framework with MBSE and life cycle cost provides an enhanced TSE that can inform system design requirements and help decision makers select high performing designs at an affordable cost. Specifically, this paper (1) provides an overview of TSE and SBD, (2) describes the Integrated Trade-off Analysis Framework, (3) describes a methodology to implement SBD in the early design phase, and (4) demonstrates the techniques using an unmanned aerial vehicle case study. We found that the Integrated Trade-off Analysis Framework informs requirement development based upon how the requirements affect the feasible tradespace. Additionally, the integrated framework that uses SBD better explores the design space compared to traditional methods by finding a larger set of feasible designs early in the design process. Full article
(This article belongs to the Special Issue Model-Based Systems Engineering)
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Open AccessFeature PaperArticle Architectural Framework for Exploring Adaptive Human-Machine Teaming Options in Simulated Dynamic Environments
Received: 1 November 2018 / Revised: 6 December 2018 / Accepted: 10 December 2018 / Published: 13 December 2018
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Abstract
With the growing complexity of environments in which systems are expected to operate, adaptive human-machine teaming (HMT) has emerged as a key area of research. While human teams have been extensively studied in the psychological and training literature, and agent teams have been [...] Read more.
With the growing complexity of environments in which systems are expected to operate, adaptive human-machine teaming (HMT) has emerged as a key area of research. While human teams have been extensively studied in the psychological and training literature, and agent teams have been investigated in the artificial intelligence research community, the commitment to research in HMT is relatively new and fueled by several technological advances such as electrophysiological sensors, cognitive modeling, machine learning, and adaptive/adaptable human-machine systems. This paper presents an architectural framework for investigating HMT options in various simulated operational contexts including responding to systemic failures and external disruptions. The paper specifically discusses new and novel roles for machines made possible by new technology and offers key insights into adaptive human-machine teams. Landed aircraft perimeter security is used as an illustrative example of an adaptive cyber-physical-human system (CPHS). This example is used to illuminate the use of the HMT framework in identifying the different human and machine roles involved in this scenario. The framework is domain-independent and can be applied to both defense and civilian adaptive HMT. The paper concludes with recommendations for advancing the state-of-the-art in HMT. Full article
(This article belongs to the Special Issue Model-Based Systems Engineering)
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Open AccessArticle MBSE with/out Simulation: State of the Art and Way Forward
Received: 11 October 2018 / Revised: 7 November 2018 / Accepted: 9 November 2018 / Published: 15 November 2018
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Abstract
The limitations of model-based support for engineering complex systems include limited capability to develop multifaceted models as well as their analysis with robust reliable simulation engines. Lack of such Modeling and Simulation (M&S) infrastructure leads to knowledge gaps in engineering such complex systems [...] Read more.
The limitations of model-based support for engineering complex systems include limited capability to develop multifaceted models as well as their analysis with robust reliable simulation engines. Lack of such Modeling and Simulation (M&S) infrastructure leads to knowledge gaps in engineering such complex systems and these gaps appear as epistemological emergent behaviors. In response, an initiative is underway to bring Model-Based Systems Engineering (MBSE) closer together with model-based simulation developments. M&S represents a core capability and is needed to address today’s complex, adaptive, systems of systems engineering challenges. This paper considers the problems raised by MBSE taken as a modeling activity without the support of full strength integrated simulation capability and the potential for, and possible forms of, closer integration between the two streams. An example of a system engineering application, an unmanned vehicle fleet providing emergency ambulance service, is examined as an application of the kind of multifaceted M&S methodology required to effectively deal with such systems. Full article
(This article belongs to the Special Issue Model-Based Systems Engineering)
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Open AccessConcept Paper An MBSE Approach for Development of Resilient Automated Automotive Systems
Received: 26 November 2018 / Revised: 28 December 2018 / Accepted: 7 January 2019 / Published: 10 January 2019
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Abstract
Advanced driver assistance and automated driving systems must operate in complex environments and make safety-critical decisions. Resilient behavior of these systems in their targeted operation design domain is essential. In this paper, we describe developments in our Model-Based Systems Engineering (MBSE) approach to [...] Read more.
Advanced driver assistance and automated driving systems must operate in complex environments and make safety-critical decisions. Resilient behavior of these systems in their targeted operation design domain is essential. In this paper, we describe developments in our Model-Based Systems Engineering (MBSE) approach to develop resilient safety-critical automated systems. An MBSE approach provides the ability to provide guarantees about system behavior and potentially reduces dependence on in-vehicle testing through the use of rigorous models and extensive simulation. We are applying MBSE methods to two key aspects of developing resilient systems: (1) ensuring resilient behavior through the use of Resilience Contracts for system decision making; and (2) applying simulation-based testing methods to verify the system handles all known scenarios and to validate the system against potential unknown scenarios. Resilience Contracts make use of contract-based design methods and Partially Observable Markov Decision Processes (POMDP), which allow the system to model potential uncertainty in the sensed environment and thus make more resilient decisions. The simulation-based testing methodology provides a structured approach to evaluate the operation of the target system in a wide variety of operating conditions and thus confirm that the expected resilient behavior has indeed been achieved. This paper provides details on the development of a utility function to support Resilience Contracts and outlines the specific test methods used to evaluate known and unknown operating scenarios. Full article
(This article belongs to the Special Issue Model-Based Systems Engineering)
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