Aditya Akundi

Integrating Model-Based Systems Engineering (MBSE) with Digital Twins (DTs) offers a promising way to transform static system models into dynamic, runtime-connected representations that enable execution, monitoring, and validation. Much current research in this area remains focused on descriptive modeling, simulation-based workflows, or unidirectional data flow, leaving a gap in methods that link architectural models directly to physical system behavior in a closed-loop system. This study fills that gap by proposing and demonstrating a four-layer framework that supports traceable, bidirectional interaction between a system’s architecture and its physical implementation. The framework consists of four interconnected layers: System Definition, Model Translation and Integration, Execution and Monitoring, and Feedback and Synchronization. The System Definition Layer captures mission goals, functional responsibilities, subsystem decomposition, and mission parameters in Capella using the Arcadia methodology. The Model Translation and Integration Layer translates these properties into executable commands via Python4Capella, converting architecture-level parameters into actionable instructions. The Execution and Monitoring Layer executes these commands in MATLAB, while the Feedback and Synchronization Layer returns runtime data to the model, supporting validation, model awareness, and refinement. A UAV case study is used to validate the framework: the UAV architecture is decomposed into key logical subsystems, and mission behaviors such as takeoff, movement, turning, and landing were modeled parametrically using the Property Values Management Tool (PVMT). These properties were translated into MATLAB commands, executed on a physical UAV, and evaluated based on telemetry-based mission distance accuracy. This work demonstrates that an MBSE model can serve not only as a design artifact but also as an authoritative, execution-connected component of a digital twin workflow. The framework contributes to MBSE-driven digital twin research by providing a structured process for integrating architecture, translation, execution, monitoring, and synchronization in a traceable manner. Overall, the study provides a scalable foundation for future developments in hardware-in-the-loop testing and digital twin applications for cyber–physical systems, and further empirical evaluation is needed to assess the framework’s transferability across domains.

Model-based systems engineering (MBSE) is being rapidly adopted in U.S. industries across various sectors. While practitioners and academics recognize many benefits of adopting MBSE, industries also report challenges such as limited tool expertise and a shortage of skilled personnel. Highlighting the difficulties in industry adoption of MBSE, prior research by the authors identified challenges such as tool limitations, knowledge gaps, cultural and political barriers, costs, and the level of customer understanding and acceptance of MBSE practices. Additionally, another study by the authors points out a gap between industry demands for MBSE skills in new hires and the current academic training programs. To further assess the MBSE industry’s workforce needs, this paper introduces a two-phase method for the Structured Extraction of MBSE competencies using large language models based on current workforce demands from LinkedIn job postings. Phase 1 involved extracting 1960 job descriptions from LinkedIn using the term “model-based systems engineer.” In phase 2, large language models (LLMs) employing deep transformer architectures were used to transform unstructured text into structured data. An AI agent was used as an autonomous software layer to manage every interaction between the raw dataset from Phase 1 and the LLM. Supported by the analyzed data, a competency framework is proposed that summarizes the tools, technical skills, and soft skills expected of a model-based systems engineer by the industry. The framework is designed to include core competencies shared across all MBSE roles, with specific competencies tailored for aerospace & defense, manufacturing and automotive, and software and IT sectors.

System modeling language (SysML) diagrams generated manually by system modelers can sometimes be prone to errors, which are time-consuming and introduce subjectivity. Natural language processing (NLP) techniques and tools to create SysML diagrams can aid in improving software and systems design processes. Though NLP effectively extracts and analyzes raw text data, such as text-based requirement documents, to assist in design specification, natural language, inherent complexity, and variability pose challenges in accurately interpreting the data. In this paper, we explore the integration of NLP with SysML to automate the generation of system models from input textual requirements. We propose a model generation framework leveraging Python and the spaCy NLP library to process text input and generate class/block definition diagrams using PlantUML for visual representation. The intent of this framework is to aid in reducing the manual effort in creating SysML v1.6 diagrams—class/block definition diagrams in this case. We evaluate the effectiveness of the framework using precision and recall measures. The contribution of this paper to the systems modeling domain is two-fold. First, a review and analysis of natural language processing techniques for the automated generation of SysML diagrams are provided. Second, a framework to automatically extract textual relationships tailored for generating a class diagram/block diagram that contains the classes/blocks, their relationships, methods, and attributes is presented.

Model‐based systems engineering (MBSE) is rapidly gaining popularity among U.S. industries. Though industry practitioners and academic researchers have identified several advantages in transitioning to MBSE, several adoption challenges of MBSE in industries, such as insufficient tool knowledge, lack of skilled personnel, and resistance in organizations toward a shift to MBSE, are observed. Attesting to the challenges in industry adoption of MBSE, a previous research study by the authors characterized the adoption challenges as tools‐based, knowledge‐based, cultural, political, and cost‐related, and customer understanding and acceptance of MBSE practices. This study is motivated to explore further and address the challenge of low MBSE tool knowledge and lack of skilled personnel with MBSE knowledge for industry adoption. This paper presents a two‐phased research approach framed by an overarching question of the extent to which the MBSE academic curriculum is aligned with industry workforce requirements. In Phase 1 of the study, we survey industry professionals from Defense, Aerospace, Automotive, and other industry clusters to identify MBSE tools, languages, and concepts preferred by industry professionals in a candidate for hire. This is followed by Phase 2 of the survey targeted at academic institutions with Systems and MBSE programs to analyze the extent to which MBSE curricula reflect industry workforce hiring requirements. Further, we also identify the challenges reported in academic institutions in training the Workforce on MBSE. The contributions of this paper are two‐fold: providing a pathway for academic institutions to align their curricula to MBSE industry workforce requirements and triggering discussion in the broader MBSE community to identify strategies for addressing MBSE adoption challenges and training future model‐based systems engineers.

Recently, environmentally friendly and sustainable materials are being developed, searching for biocompatible and efficient materials which could be incorporated into diverse industries and fields. Natural esters are investigated and have emerged as eco-friendly high-performance alternatives to mineral fluids. This research shows the evaluations on thermal transport and tribological properties of halloysite nanotubular structures (HNS) reinforcing natural ester lubricant at various filler fractions (0.01, 0.05, and 0.10 wt.%). Nanolubricant tribotestings were evaluated under two configurations, block-on-ring, and 4-balls, to obtain the coefficient of friction (COF) and wear scar diameter (WSD), respectively. Results indicated improvements, even at merely 0.01 wt.% HNS concentration, where COF and WSD were reduced by ~66% and 8%, respectively, when compared to pure natural ester. The maximum significant improvement was observed for the 0.05 wt.% concentration, which resulted in a reduction of 87% in COF and 37% in WSD. Thermal conductivity was analyzed under a temperature scan from room temperature up to 70 °C (343 K). Results indicate that thermal conductivity is improved as the HNS concentration and testing temperature are increased. Results revealed improvements for the nanolubricants in the range of 8–16% at 50 °C (323 K) and reached a maximum of 30% at 70 °C (343 K). Therefore, this research suggests that natural ester/HNS lubricants might be used in industrial applications as green lubricants.

The use of unmanned aerial vehicles (UAVs) has seen a significant increase over time in several industries such as defense, healthcare, and agriculture to name a few. Their affordability has made it possible for industries to venture and invest in UAVs for both research and commercial purposes. In spite of their recent popularity; there remain a number of difficulties in the design representation of UAVs, including low image analysis, high cost, and time consumption. In addition, it is challenging to represent systems of systems that require multiple UAVs to work in cooperation, sharing resources, and complementing other assets on the ground or in the air. As a means of compensating for these difficulties; in this study; we use a model-based systems engineering (MBSE) approach, in which standardized diagrams are used to model and design different systems and subsystems of UAVs. SysML is widely used to support the design and analysis of many different kinds of systems and ensures consistency between the design of the system and its documentation through the use of an object-oriented model. In addition, SysML supports the modeling of both hardware and software, which will ease the representation of both the system’s architecture and flow of information. The following paper will follow the Magic Grid methodology to model a UAV system across the SysML four pillars and integration of SysML model with external script-based simulation tools, namely, MATLAB and OpenMDAO. These pillars are expressed within standard diagram views to describe the structural, behavior, requirements, and parametric aspect of the UAV. Finally, the paper will demonstrate how to utilize the simulation capability of the SysML model to verify a functional requirement.

The term Industry 4.0, coined to be the fourth industrial revolution, refers to a higher level of automation for operational productivity and efficiency by connecting virtual and physical worlds in an industry. With Industry 4.0 being unable to address and meet increased drive of personalization, the term Industry 5.0 was coined for addressing personalized manufacturing and empowering humans in manufacturing processes. The onset of the term Industry 5.0 is observed to have various views of how it is defined and what constitutes the reconciliation between humans and machines. This serves as the motivation of this paper in identifying and analyzing the various themes and research trends of what Industry 5.0 is using text mining tools and techniques. Toward this, the abstracts of 196 published papers based on the keyword “Industry 5.0” search in IEEE, science direct and MDPI data bases were extracted. Data cleaning and preprocessing were performed for further analysis to apply text mining techniques of key terms extraction and frequency analysis. Further topic mining i.e., unsupervised machine learning method was used for exploring the data. It is observed that the terms artificial intelligence (AI), big data, supply chain, digital transformation, machine learning, internet of things (IoT), are among the most often used and among several enablers that have been identified by researchers to drive Industry 5.0. Five major themes of Industry 5.0 addressing, supply chain evaluation and optimization, enterprise innovation and digitization, smart and sustainable manufacturing, transformation driven by IoT, AI, and Big Data, and Human-machine connectivity were classified among the published literature, highlighting the research themes that can be further explored. It is observed that the theme of Industry 5.0 as a gateway towards human machine connectivity and co-existence is gaining more interest among the research community in the recent years.

A significant increase in System-of-Systems (SoS) is currently observed in the social and technical domains. As a result of the increasing number of constituent system components, Systems of Systems are becoming larger and more complex. Recent research efforts have highlighted the importance of identifying innovative statistical and theoretical approaches for analyzing complex systems to better understand how they work. This paper portrays the use of an agnostic two-stage examination structure for complex systems aimed towards developing an information theory-based approach to analyze complex technical and socio-technical systems. Towards the goal of characterizing system complexity with information entropy, work was carried out in exploring the potential application of entropy to a simulated case study to illustrate its applicability and to establish the use of information theory within the broad horizon of complex systems. Although previous efforts have been made to use entropy for understanding complexity, this paper provides a basic foundation for identifying a framework to characterize complexity, in order to analyze and assess complex systems in different operational domains.