Ground Based Simulation of Airplane Upset Recovery Using an Enhanced Aircraft Model
Bibliographic record
Abstract
Loss-of-control has become the dominating cause of worldwide commercial airplane accidents in recent years. Airplane upset, which could result in loss-of-control, is a situation where the aircraft goes beyond the normal ight envelope. In response to the increasing number of loss-of-control accidents resulting from airplane upsets, various preventative and recovery strategies have been proposed in the industry. One strategy considered is using ground-based ight simulators for upset recovery training. However, for the training to be meaningful, improvements must be made to the ight model aerodynamic database and the motion cues produced at upset conditions. The on-going research at the University of Toronto intends to address both of these areas with the ultimate goal to develop simulator requirements to support meaningful upset recovery training. As the rst step in the research, the aerodynamic database of an existing large transport aircraft model was extended to cover a much larger ight envelope using the wind-tunnel data from NASA Langley Research Center. This enhanced aircraft model was then used to run a set of representative upset recovery maneuvers in the simulator without motion. The time histories recorded from these upset recovery maneuvers will be used to outline areas of improvements required to the simulator motion drive algorithm (MDA) for supporting upset motions. This paper will focus on the development of the aircraft model and the simulator upset recovery experiments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".