Rotor blade optimization and flight testing of a small UAV rotorcraft
Bibliographic record
Abstract
Rotor blade optimization with blade airfoil Reynolds numbers between 100 000 and 500 000 — characteristic of small single-rotor unmanned aerial vehicles (UAV) — was performed for hover using blade element momentum theory (BEMT) and demonstrated via flight tests. BEMT was used to test various airfoil profiles and rotor blade shapes using airfoil data from 2D computational fluid dynamics simulations with Reynolds numbers representative of the blade elements. Selected blade designs were manufactured and flight tested on a Blade 600X single main-rotor UAV (671 mm blade radius) to validate the theoretical results. The parameters considered during the optimization process were the rotor frequency, radius, taper ratio, twist, chord length, airfoil profile, and blade number. The best of the improved blade designs increased the figure of merit, a measure of rotor efficiency, from 0.31 to 0.68 and reduced power consumption by 54%. Reducing the rotational frequency accounted for 45% of the improvement in power consumption, while the taper ratio and blade number accounted for 25% and 17%, respectively. The blade twist and airfoil profile only had a minor effect on the power consumption, contributing 7% and 6% to the improvement. The rotor diameter and root chord were kept identical to the original rotor and hence had no contribution. The presented results could serve as useful guidelines to single-rotor UAV manufacturers and operators for increasing endurance and payload capabilities.
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 | 0.000 | 0.001 |
| 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".