Aircraft Design for Safety in Emergency Landing
Bibliographic record
Abstract
This article addresses the problem safety in emergency landing of an aircraft belonging to the category of transport airplanes. Emergency landing poses a significant challenge to the designers of such aircraft. In this case, the safety requirements aimed at avoiding serious injury to occupants (passengers and crew members) are combined with the uncertainty of the loads acting on impact with the landing surface and the behavior of the aircraft structure, the elements of which are destroyed during the impact. The provisions of airworthiness standards and existing design approaches mainly assume the conditions of a “soft” emergency landing (called a “minor crash landing”, corresponding to minor damage and injury), while in other possible scenarios the chances of survival of occupants are not guaranteed, and there is a safety deficit. To improve safety in this situation, a new aircraft design concept is proposed – Smart, Pro-Active, Resilient System (SPARS). It is applicable to the creation of various complex, safety-critical and expensive technical systems, the operation of which may involve extreme manifestations of uncertainty that exceed the design limits. The SPARS concept combines defense in depth against predictable hazards, in-service monitoring and diagnostics of anomalies with the ideas of a biologically similar (bionic) response of the system to adverse events, including unexpected ones, and giving it the ability to recover from destructive impacts. This article illustrates the SPARS concept using a hypothetical example of emergency landing of the Soviet aerospace vehicle 'Buran', similar to the American 'Space Shuttle'. However, the provisions presented are also applicable to conventional aircraft, including civil airplanes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".