Development and Testing of an Adaptive Motion Drive Algorithm for Upset Recovery Training
Bibliographic record
Abstract
The necessity of platform motions for upset recovery training is a somewhat controversial topic. Even with a flight model that has an extended aerodynamic database to correctly simulate the aircraft response outside the nominal flight envelope, it is still uncertain how well the typical ground-based simulator’s hexapod motion system that has limited travel can simulate the large amplitude, low frequency motions during upsets. To address these issues, a new adaptive motion drive algorithm was designed to maximize the fidelity of the simulator cues for a typical hexapod motion system during upset recovery maneuvers. During the design and tuning of this new algorithm it was determined that for severe upset events both specific force and angular rate cannot be of at least medium fidelity simultaneously. Therefore, a paired comparison experiment was run for a representative set of upset scenarios to analyze the effects of different trade-offs in specific force and angular rate on pilot subjective fidelity and recovery performance. A preliminary analysis of the data found that for scenarios where the aircraft remained below stall and hence directionally stable, there was strong preference for motion that minimized lateral specific force false cues at the expense of good angular cues. For scenarios where the aircraft became unstable, there was little subjective preference between specific force or angular rate cues but pilot performance improved when good angular rate cues were present.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".