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 radio controlled (RC) helicopters - was performed 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 (CFD) simulations with Reynolds numbers representative of the blade elements. A blade design utilizing a cambered profile, taper and twist was developed for increased performance in hover. Selected blade designs were manufactured and flight tested on a Blade 600X RC helicopter (671 mm blade radius) to validate the theoretical results. The best of the improved blade designs increased the Figure of Merit (FM) by 20% and reduced power consumption by 22% while keeping the rotational frequency constant. Reducing the rotational frequency from 2,000 to 1,500 RPM resulted in an additional 55% increase in the FM and 35% reduction in the power consumption, while a one-bladed design further improved endurance and range performance of the helicopter by as much as 20%. The presented results could serve as useful guidelines to small Unmanned Aerial Vehicle (UAV) helicopter manufacturers and operators for increasing endurance, range and payload capabilities.
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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 | 0.000 |
| 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".