Optimization of Leading Edge Tubercles Applied to Helicopter Rotor Blades
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2021-0733.vid The application of leading edge tubercles to rotor blades, with a constant amplitude and wavelength shape, have been previously explored showing improvements in figure of merit from increased thrust generation and reduced power requirements. This paper investigates a multi-objective optimization of rotor blades for increased figure of merit in hover and reduced power in forward flight by selecting the best tubercle amplitude and wavelength shapes along the rotor span. A blade element theory is employed for fast aerodynamic analysis using a sectional aerodynamic properties database at different radial locations. The database is populated using computational fluid dynamic simulations of rotors with different constant tubercles shapes and flow conditions. Pareto frontier results suggest increase in figure of merit by 45% and reduction in power coefficient of 3.5% can be achieved for optimal rotor tubercle configurations with non-uniform tubercle shape distributions. Post-optimal computational fluid dynamics supports the findings of the multi-objective optimization and elucidates tubercle performance enhancements from the change in flow behaviour.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".