Systems architecting: a practical example of design space modeling and safety-based filtering within the AGILE4.0 project
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it