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Systems
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Digital Engineering: Transformational Tools and Strategies
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Digital Engineering: Transformational Tools and Strategies

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

Deadline for manuscript submissions: 31 August 2026 | Viewed by 5299

Editors

Dr. Lawrence Dale Thomas

E-Mail Website
Guest Editor
Department of Industrial & Systems Engineering and Engineering Management (ISEEM), University of Alabama in Huntsville (UAH), Huntsville, AL, USA
Interests: model based systems engineering; systems integration; systems science; digital engineering; digital transformation
Special Issues, Collections and Topics in MDPI journals
Dr. Ana Wooley

E-Mail Website
Guest Editor
Department of Industrial & Systems Engineering and Engineering Management (ISEEM), University of Alabama in Huntsville (UAH), Huntsville, AL, USA
Interests: digital twin; simulation; smart manufacturing; lean manufacturing; digital engineering
Dr. Hanumanthrao Kannan

E-Mail Website
Guest Editor
Department of Industrial & Systems Engineering and Engineering Management (ISEEM), University of Alabama in Huntsville (UAH), Huntsville, AL, USA
Interests: systems engineering theory; formal needs & requirements; functional architecture; verification and validation; formal logic; knowledge representation & reasoning

Special Issue Information

Dear Colleagues,

Digital engineering is a comprehensive approach to product and system development that integrates digital technologies, tools, and methodologies throughout the entire engineering lifecycle. It involves using digital models, simulations, data analytics, and collaborative platforms to design, test, and optimize products virtually before physical implementation, enabling engineers to reduce development time, minimize costs, and improve product quality by identifying and resolving issues early in the design process. After physical implementation during operations and sustainment, digital engineering provides value through digital twins that monitor real-time system performance, predictive maintenance algorithms that anticipate failures, and data-driven insights that optimize operational efficiency and extend asset lifecycles, while also enabling seamless system upgrades and new technology insertion by allowing engineers to virtually test compatibility, assess integration impacts, and validate performance improvements before implementing changes to operational systems. Digital engineering also facilitates better collaboration across multidisciplinary teams and supports continuous improvement through real-time data feedback and iterative design processes.

Despite its benefits, digital engineering faces significant implementation challenges. This Special Issue invites contributions proposing new, innovative, and original approaches to address digital engineering challenges, including the following:

  • Digital engineering transformation in organizations, which requires substantial cultural shifts from traditional engineering practices, extensive workforce retraining, and significant upfront investments in new tools and infrastructure.
  • Data integration and interoperability when connecting legacy systems with modern platforms while ensuring data quality, security, and governance; and the development of trustworthy digital twins through formal approaches, including rigorous “fit-for-purpose” analysis, ontology-driven representations, and formal verification and validation (V&V) frameworks.
  • The rapid pace of technological change can make it difficult to select and maintain appropriate digital tools, and organizations may face resistance to change from personnel accustomed to established engineering workflows.
  • Applications of digital engineering across sectors and disciplines.
  • Metrics and maturity models for assessing progress, return on investment (ROI), and organizational readiness in digital engineering implementations.

This Special Issue looks to establish a forum for both academic researchers and field practitioners to contribute their knowledge and discoveries, thereby progressing the current boundaries of both research and applied practice in digital engineering.

Dr. Lawrence Dale Thomas
Dr. Ana Wooley
Dr. Hanumanthrao Kannan
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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.

Keywords

  • digital engineering
  • digital transformation
  • digital engineering applications
  • digital engineering metrics
  • digital engineering maturity models
  • data integration and interoperability

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

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24 pages, 653 KB  
Article
Specifying Simulation Commitments Early in System Design: Introduction to the EFSUT Methodology
by Bernard P. Zeigler
Systems 2026, 14(6), 691; https://doi.org/10.3390/systems14060691 - 16 Jun 2026
Viewed by 151
Abstract
Engineering programs increasingly rely on simulation for early design decisions, yet a simulation’s obligations—the specific questions it must answer and observations it must provide—are often left implicit until late in development. The Experimental Frame–System Under Test (EFSUT) methodology addresses this gap by requiring [...] Read more.
Engineering programs increasingly rely on simulation for early design decisions, yet a simulation’s obligations—the specific questions it must answer and observations it must provide—are often left implicit until late in development. The Experimental Frame–System Under Test (EFSUT) methodology addresses this gap by requiring these commitments to be specified explicitly early in system design. EFSUT organizes simulation around four core elements—questions, experimental conditions, models, and results—and defines formal relations that clarify how tests are derived and which models can meaningfully be evaluated. We demonstrate the methodology in two contrasting domains, showing how an informally stated motivating question leads to a structured sequence of experimental conditions and guides model selection and interpretation. Compared with existing approaches to model adequacy and digital-engineering traceability, EFSUT provides a clear, question-driven foundation that links stakeholder intent to model evaluation in a transparent, defensible manner. This approach is particularly valuable for aligning simulation practice in multi-model and system-of-systems contexts. Future work includes the automated derivation of Experimental Frames, integration with digital-thread toolchains, and more broadly, development of a lifecycle-spanning, formalized question-driven framework to support evaluation, optimization, and adaptation where traditional requirement-based methods fall short. Full article
(This article belongs to the Special Issue Digital Engineering: Transformational Tools and Strategies)
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25 pages, 1018 KB  
Article
Ontology Quality Improvement in the Semantic Web: Evidence from Educational Knowledge Graphs
by Wassim Jaziri and Najla Sassi
Systems 2026, 14(2), 154; https://doi.org/10.3390/systems14020154 - 31 Jan 2026
Viewed by 1699
Abstract
Intelligent systems draw much of their reliability from the quality of their ontologies; however, manual ontology assessment remains patchy, time-consuming, and difficult to scale. To address these limitations, this paper proposes a domain-independent, machine-learning-driven framework for ontology quality assessment and improvement in the [...] Read more.
Intelligent systems draw much of their reliability from the quality of their ontologies; however, manual ontology assessment remains patchy, time-consuming, and difficult to scale. To address these limitations, this paper proposes a domain-independent, machine-learning-driven framework for ontology quality assessment and improvement in the Semantic Web. The framework combines structural, semantic, and documentation metrics with supervised learning models to predict quality issues and recommend targeted refinements through a four-phase workflow comprising ML model development, metric definition, automated improvement, and empirical evaluation. The approach is validated on educational knowledge graphs using 1500 ontology modules from the EDUKG repository, including a 100-module expert-annotated gold set (κ = 0.82). Experimental results show structural precision of 93.5% and semantic precision of 90.2%, with overall F1-scores close to 90%, while reducing ontology development time by 42% and quality assessment time by 65%. These findings demonstrate that coupling ML with structured quality metrics substantially enhances ontology reliability while preserving pedagogical and operational relevance in educational settings. Although empirical validation is conducted in the education domain, the modular and ontology-agnostic architecture can be adapted to other knowledge-intensive domains through retraining and domain-specific calibration, offering a reproducible foundation for continuous ontology quality improvement in Semantic Web applications. Full article
(This article belongs to the Special Issue Digital Engineering: Transformational Tools and Strategies)
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33 pages, 7044 KB  
Article
A Digital Engineering Framework for Piston Pin Bearings via Multi-Physics Thermo-Elasto-Hydrodynamic Modeling
by Zhiyuan Shu and Tian Tian
Systems 2026, 14(1), 77; https://doi.org/10.3390/systems14010077 - 11 Jan 2026
Viewed by 555
Abstract
The piston pin operates under severe mechanical and thermal conditions, making accurate lubrication prediction essential for engine durability. This study presents a comprehensive digital engineering framework for piston pin bearings, built upon a fully coupled thermo-elasto-hydrodynamic (TEHD) formulation. The framework integrates: (1) a [...] Read more.
The piston pin operates under severe mechanical and thermal conditions, making accurate lubrication prediction essential for engine durability. This study presents a comprehensive digital engineering framework for piston pin bearings, built upon a fully coupled thermo-elasto-hydrodynamic (TEHD) formulation. The framework integrates: (1) a Reynolds-equation hydrodynamic solver with temperature-/pressure-dependent viscosity and cavitation; (2) elastic deformation obtained from FEA (finite element analysis)-based compliance matrices; (3) a break-in module that iteratively adjusts surface profiles before steady-state simulation; (4) a three-body heat transfer model resolving heat conduction, convection, and solid–liquid interfacial heat exchange. Applied to a heavy-duty diesel engine, the framework reproduces experimentally observed behaviors, including bottom-edge rounding at the small end and the slow unidirectional drift of the floating pin. By integrating multi-physics modeling with design-level flexibility, this work aims to provide a robust digital twin for the piston-pin system, enabling virtual diagnostics, early-stage failure prediction, and data-driven design optimization for engine development. Full article
(This article belongs to the Special Issue Digital Engineering: Transformational Tools and Strategies)
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Other

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33 pages, 3180 KB  
Systematic Review
Digital Engineering: A Systematic Literature Review of Strategies, Components, and Implementation Challenges
by Ana Wooley and Landon Womack
Systems 2025, 13(12), 1046; https://doi.org/10.3390/systems13121046 - 21 Nov 2025
Cited by 2 | Viewed by 2173
Abstract
Digital Engineering (DE) is redefining systems engineering through data-driven methods, digital models, and integrated tools that support lifecycle decision-making. Since the release of the 2018 U.S. Department of Defense (DoD) Digital Engineering Strategy, DE has gained traction across government, industry, and academia. This [...] Read more.
Digital Engineering (DE) is redefining systems engineering through data-driven methods, digital models, and integrated tools that support lifecycle decision-making. Since the release of the 2018 U.S. Department of Defense (DoD) Digital Engineering Strategy, DE has gained traction across government, industry, and academia. This systematic literature review examines 56 peer-reviewed publications from 1995 to 2024 to synthesize current research on DE and assess its maturity as a discipline within systems engineering. The review is guided by five research questions focused on DE definitions, reported benefits, core components, implementation challenges, industry applications, and alignment with the five strategic goals of the DoD DE Strategy. The analysis reveals that while DE is widely discussed, its definitions and applications vary, and many studies lack detailed methodologies or implementation guidance. Furthermore, alignment with the DoD’s strategic goals is often implied but not explicitly addressed. Findings highlight the need for standardized frameworks, improved integration strategies, and evidence-based evaluation of DE effectiveness. This work contributes to systems engineering by identifying research gaps and offering a consolidated view of how DE is evolving across sectors. The insights are intended to inform practitioners, researchers, and policymakers as they implement and refine DE practices in complex system developments. Full article
(This article belongs to the Special Issue Digital Engineering: Transformational Tools and Strategies)
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