ABSTRACT Understanding and managing modern systems is becoming more challenging as they grow in complexity and become more integrated, as emphasized in the INCOSE Systems Engineering Vision for 2035. These new challenges increase the risk of system failure. Resilience Engineering is a subfield within Systems Engineering that studies system performance in the face of disruptions. Due to the term resilience having multiple connotations, many methods measure resilience in systems differently. Moreover, traditional methods to measure resilience, such as linear recovery or probability‐based models, can become insufficient as systems grow in size, complexity, and nonlinearity. Bifurcation Analysis (BA) is a mathematical approach to understanding system behavior under changing conditions. BA is commonly used to study nonlinear systems and transition states known as bifurcations. While it has been extensively used in many fields, BA is not widely used in Resilience Engineering. This paper suggests the BARD—Bifurcation Analysis for Resilience Diagnostics—framework to aid Resilience Engineering practitioners in addressing system resilience in complex systems. While drawing from control theory, the framework is used solely as a diagnostic instrument to help system engineers identify risks and performance regimes; the framework does not attempt to design or tune control systems. The BARD framework is applied to two cases to demonstrate its uses and potential. Contributions are an overview of the current state of Resilience Engineering and Bifurcation Analysis, the introduction of BARD as a general‐use framework to assess resilience via Bifurcation Analysis, and two applications of the framework.

ABSTRACT In current practice, a verification strategy (VS) is planned by considering various uncertainties at the beginning of a development program. All relevant uncertainties are expected to be calibrated to facilitate the evaluation of the planned verification strategies. However, not all calibration information about uncertainties may be available at the beginning and some additional activities to discover unknown uncertainties may be necessary. This paper presents a set‐based planning (SBP) methodology for designing and executing system verification strategies under knowable uncertainty (KU). Building on a Bayesian network model of verification and correction activities (CAs), we introduce preparation activities that quantify KU, enabling dynamic planning as new information becomes available. Using a notional satellite communication instrument as a case study, we compare SBP against traditional static planning across scenarios with varying estimation errors in likelihood ratio (LR) values. Results show that SBP consistently achieves higher expected values, with substantial gains when CAs have low success rates. The findings provide practical guidance on when dynamic verification planning yields value and lay the groundwork for extensions to broader classes of uncertainty.

ABSTRACT Heading toward DE and the goal to provide a common, freely accessible MBSE methodology for its member states, ESA has developed the ESA MBSE Methodology. Initially based on SysML v1, it has been applied in numerous space and non‐space projects. With the anticipated release of SysML v2, introducing substantial updates to the language and its application in SE, there is growing interest in assessing its impact. This paper investigates the potential benefits and challenges associated to this transition through a pilot SysML v2 implementation of the ESA MBSE Methodology. A structured approach was followed: identifying the major changes introduced by SysML v2 within the scope of the available tools, then employing an iterative process to map ESA MBSE Methodology concepts to SysML v2 elements. A SysML v2 language extension in textual notation was developed, and applied to a case study space mission. A comparison of the graphical notation diagrams generated in SysML v1 and v2 was also performed, leading into key insights of differences between the SysML v1 and v2 implementations. Notable differences are observed in the way definitions and usages can be exploited by the methodology, as well as how Functions can be implemented and used. This work provides a basis for further methodology refinement.

ABSTRACT Moving systems engineering's center of gravity from text documents to digital models, model‐based system engineering (MBSE) facilitates collaboration, reduces errors, and improves system understanding, but systems engineers often face challenges when they start working with MBSE, as they need to consider and model textual requirements as a basis for the system architecture. Synchronizing requirements with the system model can address critical questions, such as which requirements are assigned and covered in the system model, what effort is required to meet these requirements, how to manage changes to requirements, and how to effectively trace them in the system. Object Process Methodology (OPM, ISO 19450) is an MBSE language and methodology for modeling systems, which encapsulates function, structure, and behavior within a single model. The research goal is to expand the MBSE methodology by modeling requirements that are linked to system model artifacts. The objective is minimizing misunderstandings between stakeholders’ text‐based requirements and the engineered model while improving the ability to track changes in requirements and artifacts. An OPM‐based model, OPM‐R, which unifies system requirements and architecture, was developed and evaluated through (1) interviews with seven experts who compared the current text‐based approach and the proposed one based on the requirements of three test system models and (2) a researcher diary. Findings indicate that OPM‐R is preferred over the text‐based approach, as it reduces misunderstandings and gaps in requirements, enhancing the ability to trace coverage of system artifacts and track changes in requirements.

ABSTRACTSystems engineering (SE) is increasingly centered on advancing from model‐based systems engineering (MBSE) to semantic‐based systems engineering (SBSE) and further integrating Digital Twin technologies. This evolution is driven by the development and use of ontologies, which provide the flexibility needed to address key challenges in MBSE, such as resolving semantic ambiguities and improving data exchange. The European Space Agency (ESA) exemplifies this shift, with numerous initiatives to improve cross‐disciplinary collaboration and data integration, positioning SE at the heart of these efforts. By incorporating modular domain ontologies and embracing semantic technologies, ESA is driving new collaborative design capabilities within SE. This paper reviews the critical role of semantic approaches in empowering SE practices and delves into their application, benefits, and future research directions. Key ESA initiatives are highlighted, alongside a demonstration of a mission that simulates this approach on a small scale.

ABSTRACT This paper presents an adaptable system for disaggregated and distributed chat communications that applies Artificial Intelligence (AI) to support time‐critical battlespace environments. In such environments, operators may have dozens of chat conversations ongoing simultaneously. Although some of these messages are sorted according to keywords to help the operators better prioritize them, these keywords are subject to typing errors and other corruptions that may lead to the operator missing key decision‐making information. Considering the present expansion of the battlespace to include multimission and multidomain, the number of chat sessions is greatly increasing. The challenge for mission engineering in this environment is how to reduce and prioritize the vastly expanding collection of chat messages so that operators focus only on those chat messages that are most critical to meeting mission success and to do so accurately and dynamically. The approach presented here introduces a real‐time tasking capability within a fully autonomous adaptable system for Disaggregated Distributed AI Chat Enablement (D2ACE) to support mission engineering. D2ACE applies AI/ML techniques to correct spelling, typos, and other corruptions in chat messages; to adapt to recognize uncommon language formats; and to prioritize and reduce the quantity of chat messages to only those relevant to specific commander's objectives and intent for the mission. D2ACE accomplishes these goals while maintaining security at a high level such that only authorized individuals have access to their respective relevant chat messages.

ABSTRACT Systems engineering's practice is informed by many hundreds of guiding propositions, but their diverse nature and inconsistent categorization hinder systematic aggregation and refinement. This paper introduces a framework for understanding the origins and evolution of these propositions and proposes a unifying classification scheme. We propose a maturation model that assesses guiding propositions in terms of measures we call scope, authority, and capability. These measures jointly determine the maturity of a guiding proposition. We describe a strategy for systematically improving the maturity of propositions, advancing them from context‐specific patterns to becoming generally applicable principles. We show how the maturation process supports the evolution of greater engineering elegance and greater transdisciplinarity. We argue that such advances will be needed for SE to effectively address the unprecedented challenges likely to emerge in Industry 4.0 and Society 5.0.

ABSTRACT Developing complex systems like satellites and aircraft organizations involve numerous decision‐makers across various levels. These stakeholders, internal or external, express preferences traditionally through textual needs statements. Textual needs statements are easy to formulate and use, but offer limited analytical capacity. Alternative approaches based on decision analysis facilitate representing preferences rigorously but prove resource intensive without accuracy guarantees. Recently, researchers have proposed modal preference logic (MPL) to mathematically represent and reason about stakeholder preferences in systems engineering. Past work has demonstrated MPL's promise in identifying preference inconsistencies. This paper builds on that foundation to advance new theory and methods for systematically measuring detected inconsistencies. It introduces the Inconsistency Magnitude (IM), a rigorously developed metric grounded in minimal inconsistent subsets that sensitively quantifies inconsistencies and the spread of inconsistencies. Underpinning this metric, several theorems are presented that prove key properties across syntax variations. The research computationally exemplifies the metric over sample preference bases exhibiting differing complexity levels of inconsistencies.