Bibliographic record
Abstract
The following study explores the effect of yoke size on propeller performance. The yoke is the part which attaches the propeller to the rotor, this acts as a sort of a moment arm, which when extended the local speed of the blade will be higher due to the increase in radius. The method used to test the performance of the yoke will be based on blade element momentum theory. This is done by comparing the performance of the geometric model created to actual wind tunnel data. Although there won’t be a perfect match, their results should be close and have a small difference for more accurate results. The propeller used and tested is the Aeronaut 20x8. The 20x8 is being tested for the means of improving the overall power train efficiency of CREATeV, a lightweight solar powered aircraft built for endurance. The flight speeds considered for the analysis are between 8-12, with an rpm range of 2000 to 5500. The results find that increasing the yoke size increases the propeller efficiency by up to 4%. With a decreased power requirement during cruise for CREATeV. The yoke size for maximum propeller efficiency does not change much with speed, indicating that the yoke size mainly depends on the geometry of the propeller more than it does flight conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".