Influence of the Path Pitch Angle on the Flyability and Mode of Motion of Propeller Airplanes
Bibliographic record
Abstract
This study examines the distance that an airplane is able to fly on inclined trajectories at various angles when it starts at different altitudes. It clearly exhibits the dependence of the airplane’s performance, when it climbs or descends, on the angle of inclination of the path and on the variations in initial weight, altitude, and power available. The results are obtained by solving the airplane’s equations of motion with the airplane’s dynamic constraints. Different specific dynamic behaviors of the airplanes are shown to occur within particular ranges of the inclination angle. Two remarkable behaviors, which have not been discussed before, are exhibited, namely the existence of a “relative ceiling” in ascending trajectories and that of a “maximum gliding altitude” in descending trajectories. These are specific altitudes that delimit the ranges of altitudes from which the airplane always attains the same terminal speed and reaches the same final altitude, whatever its initial altitude. Finally, it is shown how a feasibility matrix can be constructed, with which one can rapidly determine if a considered trajectory is flyable or not. This matrix requires only a small amount of memory storage and could reside on board essentially any airplane. The results of the study are illustrated with two very different airplanes: a Cessna 182 Skylane and a Silver Fox-like small UAV.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".