bluuebunny/crossref_metadata_2025_split huggingface dataset:
Case Studies for Complexity Pattern Identification
Authors: Pickard Andrew C , Beasley Richard , Beale Dean , McKinney Dorothy , Oosthuizen Rudolph , Stewart Dave , Cureton Kenneth , Mirchandani Chandru 
Published: September 2024

AbstractThe INCOSE Complex Systems Working Group Heuristics Team has selected 67 Principles and Heuristics that are considered to be particularly relevant to Complex Systems. These have been incorporated into a Difficulty Assessment Tool that prioritizes the list of Principles and Heuristics based on scoring of a matrix of four Difficulty Elements and six System Elements (to characterize the nature of the complexity).The purpose of this paper is to describe an initial assessment of the effectiveness and usefulness of the Difficulty Assessment Tool. The Tool has been used to assess eight Case Studies by five assessment teams — one with three people working together, one with two people and the remaining three with individual assessments.The results of these assessments have been compared using four different correlation methods, using the total weighted Heuristic score, the maximum weighted Heuristic score, a Match / Mismatch analysis of the top fifteen and bottom seven Heuristics, and a difference ranking between pairs of assessors of all 67 Principles and Heuristics. The last two assessment methods are shown to be more insightful.The assessment teams then reviewed the relevance of the highest and lowest‐ranked Principles and Heuristics to the full Case Study definitions (Problem and Outcome). There is good agreement of relevance for the highest‐ranking Principles and Heuristics, less so for the lowest‐ranking ones. Based on this initial assessment, the DAT shows promise to help people develop complex systems.The paper concludes with recommendations for further assessment of the Difficulty Assessment Tool.
Evaluating the Scalability and Combinatorial Effectiveness of Design‐for‐Resilience Heuristics
Authors: Rogoll Olivier , Wierzbanowski Benjamin , Watson Bryan C 
Published: September 2024

AbstractThis study investigates the scalability of biologically inspired design‐for‐resilience heuristics in System of Systems (SoS) of varying sizes, simulating SoS with ten, twenty, and thirty constituent systems. We build upon previous research that identified thirteen design heuristics for SoS resilience based on Ecological Network Analysis (a subset of graph theory). We both extend their application to larger network sizes and combine them to enhance efficiency. Our analysis validates the application of design heuristics for random networks to evaluate the effectiveness of individual and combined design heuristics in network resilience compared to random network modifications. The outcomes provide insights into the scalability and efficiency of these heuristics, revealing the potential for systematic SoS design and resilience improvement. Key contributions of this research include the validation of the scalability of bio‐inspired resilience metrics and the testing of nine new combined design heuristics for SoS. These contributions provide a tool for decision‐makers seeking to select resilient SoS architectures. The findings also pave the way for future research into broader applications beyond ecological networks and further exploration of the relationship between ecological robustness and resilience.
Tactical Network Bandwidth Analysis: Application of the Wearables Model‐Based Systems Engineering ‐ System Architecture (MBSE‐SA)
Authors: Cyr Jillian , Sarathi Tara , Balcius Jim , Shatz Michael 
Published: September 2024

AbstractWarfighters are often exposed to harsh environmental conditions, and experience high rates of physical and cognitive stress, fatigue, and infections, resulting in the degradation of their health and physical performance. This degradation can have a profound effect on the readiness of military forces. Wearable sensor systems can be used to monitor warfighter physiological and cognitive data, providing insight into their health status during routine military training and deployed operations; however, to enable a real‐time, tactical health and performance monitoring capability, wearable sensor systems must integrate into existing tactical military information networks without compromising network function. We extended our existing Wearables Model‐Based System Engineering – System Architecture (MBSE‐SA) to include a bandwidth simulation to analyze the effects wearable sensor systems have on overall network function specifically for military use cases. Our Wearables MBSE‐SA enabled us to model many notional and existing architectures, which represent the wide range of wearable sensor devices, communication protocols, end user devices, and tactical network nodes typically present in operational environments. By taking advantage of the existing Wearables MBSE‐SA framework and architectures, the resulting bandwidth simulation rapidly assessed several existing military network architectures for wearable sensor system integration and identified where network changes were required. Validating the flexibility of the Wearables MBSE‐SA to incorporate new analyses was critical for the military's ability to explore wearable sensor system trades and evaluate architectures in the quickly changing wearable systems technology domain.
Risk and Systems Analysis for Renewable Power Generation with Environmental and Other Stressors
Authors: Marcellin Megan C. , Pavur Gigi , Cardenas John J. , Waheed Saddam Q. , Trump Benjamin D. , Linkov Igor , Lakshmi Venkataraman , Lambert James H. 
Published: September 2024

AbstractThe effects of climate change and water scarcity threaten the stability of critical infrastructure systems in developing regions. In particular, the interconnectedness of energy systems, natural resources, economic growth, and social welfare requires a systems‐level framework to identify scenarios which most impact these systems. This paper evaluates and quantifies infrastructure system risk, defined as the influence of scenarios on system priorities. A scenario‐based multi‐criteria preferences model assesses system component priorities for a baseline scenario as well as climate and related scenarios. The shift in priorities between the baseline and other future scenarios define scenario disruptiveness, or level of risk. The methods are demonstrated for the case of the emerging renewable energy sector of Iraq. Twenty‐five renewable energy system assets are prioritized by an assessment of system success criteria, which include economic, social, political, and climate considerations. The system prioritization is reevaluated in the case of seven disruptive scenarios relating to water scarcity, climate volatility, and social and economic shifts. This paper advances methods of the Systems Engineering Body of Knowledge (SEBoK) Part 3: Engineering and Management, by defining system risk and proposing methods for risk identification and risk analysis.
When Moving Backward Means Moving Forward – Educating Systems Engineers in Designerly Ways of Thinking
Authors: Bengtsson Marie , Axehill Johanna Wallén , Herzog Erik 
Published: September 2024

AbstractSystems Engineering as a discipline provides many tools for managing complexity and reducing risks. However, these tools come with drawbacks when ideating new product concepts in early lifecycle phases when the problem and solutions spaces are open. This paper suggests that methods from the Design field have a complementing role early in the systems lifecycle, but that those methods need to be accompanied by a different way of approaching problems, something that takes time to learn. We present experiences from a hybrid university course, where regular students were mixed with professional systems engineers for more rapid development of Design method experience in both groups.
Institute for Convergent Systems Engineering: A Strategic Plan for Ethical Sustainability
Authors: Wade Jon , Shin Hyoduk , Gessner Richard 
Published: September 2024

AbstractThe last few years have made it clear that the world is entering a new phase in which sustainability is of paramount importance to the survival and well‐being of our global societies. This paper describes the strategic plan for the Institute for Convergent Systems Engineering which is addressing the challenge of ethical sustainability in which social, environmental, and economic implications are carefully considered and balanced. Included is a discussion of the criticality of convergent systems engineering and its values and principles. The paper also presents a three part strategy for sustainability at the macro, meso, and micro levels entailing the consideration of end‐to‐end global value supply chains. The foundational pillars of research, education, and collaboration are also described.
Accelerating Digital Transformation through MBSE, Multi‐physics Simulation and Digital Twin in Industry
Authors: MARTINEZ Jacques , BOUHALI Imane , PALLADINO Luca , IDASIAK Vincent , KRATZ Frederic , CHOLEY Jean‐Yves , MHENNI Faida 
Published: September 2024

AbstractIn the dynamic aviation industry, Aerospace Industries grapple with the intricate challenge of certifying products amidst integration requirements from aerospace manufacturers and airworthiness regulations imposed by aviation authorities. This paper underscores the indispensable role of ModelBased Systems Engineering (MBSE) in addressing the multiphase issues inherent in our product complexity. The first focal point is the certification process, where Safran SEATS navigates the complexities imposed by aerospace manufacturers and stringent airworthiness regulations. This intricate certification process demands the strategic deployment of MBSE to streamline and enhance procedural efficiency. The second challenge arises from the abstract nature of style and perceived quality requirements articulated by diverse customers. With the goal of exceeding customer expectations and creating a genuine “WOW” effect, MBSE becomes instrumental in aligning product development with diverse and often oriented customers' needs. The third dimension of complexity stems from the variability in emerging requirements, where innovations swiftly become industry standards. MBSE provides a structured approach to handle this variability, ensuring adaptability and positioning products at the forefront of innovation. Additionally, we dissect the influence of Model‐Based Design (MBD) on design processes, shedding light on its transformative effects. Introducing the concept of a digital twin, we explore its significance in the aerospace domain. Real‐world implementations of digital twin technology examined through concrete examples from aerospace projects. Finally, the presentation addresses uncertainties surrounding program efficiency, a critical consideration in the intensely competitive seat market. This paper unfolds a compelling industrial testimony, showcasing Safran SEATS' journey in overcoming the complexity of digital transformation in Aerospace Industries. It provides solutions to intricate challenges posed by certification, customers' requirements, evolving industry standards, and economic competitiveness, encapsulating the transformative impact of MBSE in our pursuit of excellence.
Application of the System‐Theoretic Process Analysis (STPA) technique to enabling systems in the rail industry
Authors: Slowey John 
Published: September 2024

AbstractInvestigations into several recent railway safety incidents have concluded that failures of enabling systems contributed significantly to the root cause of those incidents, suggesting a tendency towards poor management of safety risk associated with subsystems outside the immediate System of Interest (SoI) boundary. This paper examines whether better application of systems thinking to the analysis of hazards, through use of the System‐Theoretic Process Analysis (STPA) technique, could have enabled safety risk to be better identified and managed in the circumstances surrounding a serious derailment that occurred in Victoria, Australia in February 2020.
Integrating STPA Extended for Coordination into SysML Using RAAML
Authors: Pennington Elizabeth , Johnson Kip , Colombi John , Hobbs Kerianne 
Published: September 2024

AbstractAs Model‐Based Systems Engineering (MBSE) becomes prevalent in engineering practice, the Department of Defense (DoD) requires a consistent methodology to conduct and record system safety analyses in the system model. Systems Theoretic Process Analysis (STPA) is a relatively new safety and hazard analysis method that utilizes the principles of Systems Theory and abstraction to analyze today's complex systems. Systems Theoretic Process Analysis Extended for Coordination (STPA‐Coord) provides a framework to design safe coordination among a system‐of‐systems architecture, which is needed for next‐generation integrated military systems. This research presents results from conducting an STPA‐Coord in Systems Modeling Language (SysML) using Risk Analysis and Assessment Modeling Language (RAAML), a recent extension to SysML that provides tools and guidance for multiple safety analyses. Results describe deviations from the RAAML standard and suggest modifications to RAAML for STPA‐Coord. Results include qualitative and quantitative observations conducting an STPA‐Coord using SysML, including time required for the effort and observed benefit over document‐based methodologies.
A Classical Modernization of the V‐Model
Authors: Barnum William J. , Fisher William , Winstead Mark W. , Walsh Stephen J. 
Published: September 2024

AbstractThis paper provides new iconography for the Systems Engineering “V‐Model” or “Vee‐Model.” The “Arch Model” resets and refreshes the original iterative intent of the “V‐Model” in a modern context integrated with digital engineering (DE). We will highlight common misperceptions that reduced the efficacy of the “V‐Model” and explore how a “classical Roman engineering” metaphor can inspire a modern view of systems development based on historically successful, foundational engineering.
Modeling NASA's Procedural Requirement Processes ‐ Implications for Digital Future
Authors: Hill Terry R. , Nicoli Patricia E. , Cornford Steven L. , Morgenstern Robert M. , Chiras David M. , Barnes Patrick D. , Bendig Josh L. 
Published: September 2024

AbstractThe National Aeronautics and Space Administration (NASA) has an ongoing Digital Transformation effort and to leverage and showcase the power of Digital Transformation, an effort is underway to develop an integrated, datacentric, model representing NASA's key process requirements. The task was divided into three phases: As Is modeling, Analysis, and To Be Planning. As part of this effort, a team has completed the first Phase I of the modeling task and is nearing completion of the second phase. This effort will capture the key elements as requirements, responsibilities, allocations, roles, products, and associated lifecycle elements. The scope of modeling included NASA's NPR 7120.5 (Project and Program Management), NPR 7123.1 (Systems Engineering) and NPRs 8705.2 (Risk classification for Robotic Missions) and 8705.4 (Human‐Rating Requirements for Space Missions).This paper will summarize the approach, scope, parsing patterns applied, metamodel, and associated workflows for the As‐Is modeling. It will also summarize the results and insights gleaned during that phase, including the review process. These insights have informed the analysis and will be discussed. The analysis modeling phase will also be summarized including how the stakeholders were engaged, how the common elements were handled and dispositioned, and will also describe some of the plans for the future of NASA NPDs and NPRs.
How the INCOSE Model‐Based Capability Matrix Has Steered Model‐Based Systems Engineering Transformation at NASA
Authors: Pierce Gregory J. , Nicoli Patricia E. , Hill Terry R. , Cornford Steven L. 
Published: September 2024

AbstractThe National Aeronautics and Space Administration (NASA) is embarking on new, complex, and diverse missions to accomplish its scientific and exploration objectives, and it views digital transformation as a key enabler for those missions. The NASA Model‐Based Systems Engineering (MBSE) Leadership Team (MLT) is leading the charge in the digital transformation of the systems engineering domain at NASA, and it is using the INCOSE Model‐Based Capability Matrix (MBCM) as a roadmap. This paper discusses the modifications and tailoring of the INCOSE MBCM (Hale & Hoheb, 2020) for use at NASA, the process the team has taken on multiple rounds of assessment, findings to date, and work products that have been generated as a result of the assessment. The paper will also discuss findings and potential changes that should be made to the original product.
Design Basis Model for Hosting Small Modular Reactors
Authors: Claghorn Ron 
Published: September 2024

AbstractTo provide energy security and head off further increases in global temperatures, an aggressive transition from fossil fuels to other types of energy implies the need to possibly construct hundreds of nuclear power plants in the near future. However, the real and perceived risks of nuclear energy remain a significant impediment to that transition.This paper describes a comprehensive work process that combines the rigor of model‐based systems engineering (MBSE) with 1) the Idaho National Laboratory's (INL) decades of experience with small reactors and with 2) modern project delivery processes. The objective is to reduce the risks of building new facilities or converting existing facilities to nuclear power generation.
Model‐Based Systems Engineering (MBSE) Application in Nuclear Power Plants (NPP)
Authors: Kawamura Ken , Arifin Habibi Husain , Ong Ho Kit Robert , Brun Thomas , Chimplee Nasis , Wu Daphne 
Published: September 2024

AbstractCompanies in the nuclear power sector are constantly being challenged to improve their safety and reliability due to increasing complexity arise from evolving safety regulations, long production life, interdisciplinary collaboration, and the need for analyzing the impact of the changes in an operational life cycle. Recognizing these challenges, the paper proposes a transition to Model‐Based Systems Engineering (MBSE) as a transformative solution to improve the management of such complex systems. With this objective, this paper presents a workflow implementation that demonstrates the MBSE methodologies to define a concept model, system architecture, impact analysis, safety and reliability analysis, and operational decision‐making of Nuclear Power Plants (NPP). The paper concludes that MBSE provides a potent approach to managing NPP by employing graphical models to develop interrelated systems that has strong adaptability to heterogeneous environments and regulatory changes. The simulation results demonstrated an NPP life cycle, impact analysis, and a test case for model‐based safety and reliability analysis for regulatory compliance, operational efficiency, balance safety, and informed decision‐making in NPP. The study also leads to a number of interesting directions of future work such as synchronization through Product Lifecycle Management, integration with Building Information Modeling, Model‐Based Commissioning/Decommissioning, and Model‐Based Cyber System Security tailored for nuclear power systems.
Innovation Ecosystem Dynamics, Value and Learning I: What Can Hamilton Tell Us?
Authors: Schindel William D. 
Published: September 2024

AbstractHeld in Dublin, Ireland, IS2024 invites us to refresh understanding of contributions to systems engineering by Ireland's greatest mathematician–Sir William Rowan Hamilton (1805 ‐ 1865), Professor of Astronomy at Trinity College Dublin and Royal Astronomer of Ireland. His profound contributions to STEM deserve greater systems community attention. Supporting theory and practice, they intersect Foundations and Applications streams of INCOSE's Future of Systems Engineering (FuSE) program. Strikingly, key aspects apply to systems of all types, including socio‐technical and information systems. Hamilton abstracted the energy‐like generator of dynamics for all systems, while also generalizing momentum. Applied to the INCOSE Innovation Ecosystem Pattern as dynamics of learning, development, and life cycle management, this suggests an architecture for integration of the digital thread and machine learning in innovation enterprises, along with foundations of systems engineering as a dynamical system.
Providing tailored heuristic advice to Systems Engineers
Authors: Beale Dean , Oosthuizen Rudolph , Pickard Andy , McKinney Dorothy , Cureton Ken , Stewart Dave , Arnold Eileen 
Published: September 2024

AbstractAn INCOSE‐wide initiative has exposed at least 600 heuristics. Previous work indicates that rationalizing and simplifying this set to make it useful and memorable is difficult, if not intractable. Difficulty Assessment Tools (DATs) have been used for years to characterize the difficulty of a problem and provide tailored advice. This paper explores using a DAT to characterize the problem, and using the outputs to provide heuristic and other forms of advice. To test this approach, 50 heuristics and 10 principles were scored and embedded into an online DAT. An experiment was conducted to determine whether the DAT discussion, recommended approach, and heuristic/principles advice were useful. All teams considered the discussion very useful. As might be expected, the results indicated that the heuristic usefulness was a function of the teams' experience and familiarity with the task. The tool prioritization of suitable heuristics met developers' expectations, but was undetected by the users. This maybe because the heuristics were a hand‐picked set of 50 Heuristics from a set of 600+, meaning all were highly useful. Further work is proposed to check this assessment. The DAT usefulness results indicate that Systems Engineers should use the DAT to inform their approach throughout the lifecycle.
THE DIGITAL ENGINEERING FACTORY: CONSIDERATIONS, CURRENT STATUS, AND LESSONS LEARNED
Authors: Gregory Joe , Salado Alejandro , O'Neal Sharon , Larez Richardo , Reda CJ , Martell Niko , Martin Evan , Colson Matthew , Masterson John , Armenta David 
Published: September 2024

AbstractIn industry, the advancement of digital engineering and the digital thread aims to reduce the impact of knowledge ‘siloes’ by providing a way to integrate data across the entire system lifecycle and across multiple domains. In a typical engineering curriculum, however, courses are still treated as ‘siloes’, and students often do not have the opportunity to experience this industrially relevant approach to engineering. The Digital Engineering Factory (DEF) is a digital engineering environment under development at the University of Arizona to support engineering students. The DEF supports students by providing access to multiple engineering tools and is structured using a ‘hub‐and‐spoke’ approach to consolidate data from these tools. Through this connected architecture, students can transfer data generated in a particular course to tools for use in other courses. Connecting course activities in this way enables students to experience a complete end‐to‐end system lifecycle. At its ‘hub’, the DEF uses Violet to integrate data from multiple sources, create a digital thread, and generate a graph representation of the dataset. This knowledge graph, written in the Ontological Modeling Language (OML), can be viewed in OML Rosetta and is structured according to the University of Arizona Ontology Stack (UAOS). The use of the UAOS and OML Rosetta allows instructors to leverage semantic web technologies to support teaching activities such as grading. In this paper, the authors review the objectives of the DEF, discuss the status of the project, and highlight current limitations and lessons learned with regards to its deployment. These may be useful to inform similar developments in industrial settings.