Integration of a model-based systems engineering framework with safety assessment for early design phases: A case study for hydrogen-based aircraft fuel system architecting
Bibliographic record
Abstract
Novel hydrogen-based aircraft concepts pose significant challenges for the system development process. This paper proposes a generic, adaptable, and multidisciplinary framework for integrated model-based systems engineering (MBSE) and model-based safety assessment (MBSA) for the conceptual design of complex systems. The framework employs a multi-granularity, model-centric approach, whereby the architectural specification is utilized for design as well as query purposes as part of a qualitative and quantitative, graph-based preliminary safety assessment. For the qualitative assessment, design and safety rules based on existing standards and best practices are formalized in the model and applied to a graph-based architecture representation. Consequently, the remaining architectures are quantitatively assessed using automated fault trees. This safety-integrated approach is applied to the conceptual design of a liquid hydrogen fuel system architecture as a novel, uncertain, and complex system with many unknown system interrelations. This paper illustrates the potential of a combined MBSE-MBSA framework to streamline complex, early-stage system design and demonstrates that all qualitatively down-selected hydrogen system architecture variants also satisfy quantitative assessment. Furthermore, it is shown that the design space of novel systems is also constrained by safety and certification requirements, significantly reducing the number of actual feasible solutions. • Development of a multidisciplinary and integrated MBSE and safety assessment framework for conceptual systems architecting. • Enhance the architecture modeling approach to serve as the single source of truth for specification and safety assessments. • Adaption to the graph-based architecture representation to allow for LH2 as a fuel type. • Identification and collection of H2 design and safety rules formalized in the architecture model. • Automation of preliminary safety assessment with the integrated MBSE-MBSA framework.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".