Systems architecting: a practical example of design space modeling and safety-based filtering within the AGILE4.0 project
Bibliographic record
Abstract
The aerospace industry strives towards innovative aircraft concepts that feature increasing electrification to meet environmental and business targets. Advanced Multi-Disciplinary Analysis and Optimization (MDAO) frameworks have been developed to help evaluate these aircraft and their systems. However, the system architecting process still relies on a system architecture baseline from past aircraft programs or historical data, thereby precluding the exploration of a larger design space and identifying optimal solutions for further development. Furthermore, the evolution of system safety is a critical factor in establishing the feasibility of a system architecture solution. Therefore, there is a need to explore a large design space of system architectures for safety, certification, and performance requirements in an efficient manner. This paper presents a rule-based safety assessment approach within a systems architecting framework that demonstrates the ability to generate and filter a large design space based on safety heuristics. This approach is demonstrated using a case study for an aircraft landing gear braking system.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".