Zonal Safety and Particular Risk Analysis for Early Aircraft Design
Bibliographic record
Abstract
Safety is paramount in aircraft design, and increasing aircraft complexity necessitates safety assessments early in design. For unconventional aircraft with novel propulsion or system technologies, it becomes even more critical to investigate safety as early as possible to avoid unfeasible configurations. In this context, the particular risk analysis (PRA) and the zonal safety analysis (ZSA) are essential to assess early, as they impact the aircraft configuration. These analyses require a three-dimensional (3D) aircraft model and substantial manual effort, limiting the ability to perform rapid iterations required to support design space exploration and multidisciplinary design optimization (MDO). To analyze many aircraft configurations and system architectures, the 3D parametric model and the PRA and ZSA require automation. This paper reviews methodologies for performing the ZSA and PRA from a systems point of view and proposes parametric zone definition, identification of risk zones, and a conceptual-level analysis of the component placement strategy. The effectiveness of the proposed approach is demonstrated with an aft equipment bay of a business aircraft for varying geometrical granularity and system electrification. Overall, the presented method is a step toward integrating system safety analysis into MDO environments, thus increasing conceptual design maturity and reducing development time.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".