Jul 2026· Journal of Aerospace Information Systems· pp. 1-11· 0 citations· 8 references
TL;DR
This work presents a framework to derive principal functional-logic scenarios from critical test data and to present them visually as functional-logic parallel lifeline charts using a fuel cell-driven propulsion example.
Abstract
Current goals for emission-free aviation require novel system concepts, such as fuel cell-driven propulsion. Yet, such concepts do not exist in current aircraft. With such low operational experience, the requirements for system design may not be fully understood. Consequently, there is a rising potential for “blind spots” in the design that emerge in integration testing or even later, leading to costly adaptations. These unexpected scenarios may be discovered early by intelligent exploration during virtual, simulation-based testing. However, there is still a gap between the physical-dynamic test data and the functional-logic requirements definition by engineers. Test results may contain many data measurements. For a human engineer, it is time-consuming and error-prone to process the amount of time series data, making it impractical in an industrial setting. To enable the engineering evaluation and derivation of missing requirements, a computer-aided abstraction step is needed. This work presents a framework to derive principal functional-logic scenarios from critical test data and to present them visually. Concretely, analysis agents extend the data with discrete system states, followed by feature-based clustering. Finally, single sequences are derived for each cluster. The results are visualized as functional-logic parallel lifeline charts. The approach is evaluated using a fuel cell-driven propulsion example.
The implementation of the ground deceleration function in civil aircraft represents a critically complex process that deeply relies on the seamless collaboration of multiple onboard systems, including but not limited to braking, thrust reversal, spoiler, and steering systems. The operational logic governing these systems is highly intricate, characterized by tightly coupled interactions, stringent safety requirements, and a vast array of diverse physical and logical interfaces. This inherent complexity makes it exceptionally difficult to gain a thorough, system-level understanding of the implementation mechanisms and collaborative principles solely through traditional means of examining extensive, yet often fragmented, design documentation. The limitations of document-based analysis frequently lead to unforeseen integration conflicts, which are typically discovered late in the development cycle, resulting in substantial rework costs and project delays. To address this pervasive industry challenge, this paper selects the aircraft ground deceleration function as a representative case study and proposes an innovative, simulation-based validation methodology. This approach systematically utilizes model state machines to create a dynamic digital representation of the system-of-systems, enabling rigorous validation of aircraft deceleration requirements under various operational scenarios. By adopting this model-based systems engineering (MBSE) paradigm for mechanism representation, our approach effectively captures the nuanced coordination, timing dependencies, and dynamic interactions within the multi-system operational logic. It thereby facilitates the intuitive identification, analysis, and resolution of potential design flaws, including logical conflicts, deadlocks, race conditions, and uncovered or ambiguous requirements. Consequently, the method not only provides a robust framework for validating the aircraft’s function-related design requirements with greater confidence but also offers crucial, data-driven support for the iterative optimization and evolution of the overall functional architecture. The fundamental value proposition of this research lies in its transformative capability to convert implicit design knowledge and assumptions—originally scattered across voluminous documents, specifications, and expert minds—into an integrated set of executable, observable, and analyzable formal models. This digital thread enables systems engineers and designers to identify deep-seated integration and coordination issues proactively during the early conceptual and detailed design stages, rather than relying on discovery during the late, costly integration and testing phases. By shifting validation left in the development V-cycle, this approach significantly reduces the risk of major design changes and associated cost overruns later in the project lifecycle. Ultimately, it effectively enhances the overall maturity, safety, certifiability, and operational reliability of complex aircraft function development, paving the way for more efficient and predictable engineering processes.
Mingqian Wang, Q. Yu, Miao Yu et al.· SAE technical paper series· 0 citations
As a core segment of national high-end manufacturing, the aviation industry relies heavily on aircraft control components that directly determine flight safety and handling performance. Digital modeling has become a mainstream technical approach for developing aviation equipment. Taking typical control components such as steering gears and flight control actuators as research objects, this paper sorts out their multi-domain structural features and coupling mechanisms across mechanical, hydraulic and electrical fields, laying a theoretical foundation for modeling. To address the accuracy limitations of traditional single-domain modeling, this paper explores approaches to balancing model complexity and simulation fidelity, develops multi-system coupled digital models, and performs simulation tests. By comparing simulated outputs with actual operating condition data, this paper analyzes the causes of deviations between them. Finally, various technical bottlenecks existing in current digital modeling and simulation are summarized. The research results indicate that integrating digital twin architecture with model-driven design can effectively mitigate insufficient accuracy in single-domain modeling. Nevertheless, prominent technical obstacles remain in accurately depicting multi-physics dynamic coupling and establishing standardized model verification that covers full operating conditions. The conclusions of this research can provide clear ideas and references for optimizing multi-domain coupled modeling of aircraft and the digital development of flight control equipment.
Yuxuan Zhu· Applied and Computational En...· 0 citations
Abstract. The activities are based on the Sagittarius project, which introduces a simulation-coupled concurrent engineering framework for the preliminary design of a low-cost surface-to-air missile aimed at countering small hostile drones. The methodology integrates guidance, propulsion, aerodynamics, structures, and lethality within a unified workflow, enabling continuous exchange of boundary conditions and performance constraints across subsystems. Trajectory simulations are employed to validate the missile performance against representative mission scenarios, while warhead lethality and structural analyses are iteratively harmonized with aerodynamic and propulsion requirements to maintain design coherence. A reference case involving a target UAV flying at 60~m/s, positioned 8000~m from the defended zone and 5000~m in altitude, was used to assess nominal behavior and off-design robustness. Results demonstrate that the integrated workflow achieves consistent performance and high kill probability across different operational profiles, highlighting the benefits of multidisciplinary coupling in early-stage design. The proposed approach emphasizes the importance of scenario-driven, simulation-based interaction between subsystems as a key enabler for affordable and adaptable Counter-UAV missile solutions.
Vincenzo Jr. DI ROSA· Materials Research Proceedin...· 0 citations
Building operations are energy-inefficient. Artificial Intelligence (AI)-driven control systems promise benefits through optimization and predictive control, but deploying them in real buildings reveals a significant software engineering (SE) challenge. SE for AI practices assume digital environments where failures mean poor user experience. Buildings are different. A bad control decision wastes energy irreversibly, violates occupant comfort, or accelerates equipment wear. Although actual safety-critical failures are rare, as real building automation systems are inherently fault-tolerant, the physical and lasting nature of even minor failures fundamentally changes SE4AI requirements. Rooted in two interdisciplinary research projects in civil engineering and computer science that target the AI-driven optimization of building operations, we identify the missing perspectives in SE4AI that currently stymie the successful deployment of AI-based systems for building operations. We further share lessons learned and best practices, and discuss broader implications for engineering AI-driven building operations and cyber-physical systems more generally. Our work proposes a foundation for SE4AI in systems where failure has physical consequences - one the research agenda below will need to validate.
Philipp Zech, S. Hammes, J. Weninger et al.· 0 citations
Abstract. Aircraft preliminary design requires the capability of rapidly exploring several different configurations without compromising the accuracy of the different physics involved, such as aerodynamics, structural analysis and low observability. In this work we propose a multi-fidelity methodology combining analysis with different levels of fidelity, where the high-fidelity is meant to be supported by Reduced Order Models (ROM). The purpose is to significantly accelerate the decision process, increasing the quality of the design already in the preliminary phases. Inspired by a previous ESTECO’s experience [1], the methodology is applied by Leonardo Aeronautics in a study-case based on an example configuration of a fighter jet. The ultimate goal is to include this methodology among the tools available for preliminary aircraft design [2]. The design workflow begins with a conceptual phase that involves the rapid generation of several configurations through low-fidelity simulations. The purpose is not only to get a preliminary evaluation of the performances and requirements compliance, but also to find a region of interest in the input domain where to concentrate the analysis effort. In this region, we developed a Design of Experiments (DOE) Table containing different possible aircraft configurations to perform the high-fidelity simulations for the two selected physics: CFD for the aerodynamics and electromagnetic fields for the estimation of the radar cross-section (RCS). The resulting set is used as a training set for the development of the non-intrusive ROM models. This approach enables a detailed exploration of the solution space with limited computational costs, making it possible to converge on engineering-feasible configurations within the timeframes of the preliminary design phase.
Federico Carlini· Materials Research Proceedin...· 0 citations
This work presents a method for representativeness assessment of AI/ML constituent ODDs in the context of aviation safety assurance and illustrates how statistical distribution comparison methods can support the assessment of representativeness for safety-critical AI applications.
Thomas Stefani, Johann Maximilian Christensen, Elena Hoemann et al.· 0 citations
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