Installation and Manufacturing Effects of Propeller Trailing Edge Serrations
Bibliographic record
Abstract
Trailing edge noise is one of the dominant broadband noise sources of UAV rotors while hovering. Trailing edge serrations may help alleviate the noise without impacting the aerodynamics significantly. Theoretical models for serrated edges, such as Ayton's model, provide predictions for the trailing edge noise generated by a fully turbulent flow over an infinitesimally thin plane. This study assesses the validity of these assumptions by considering serration installation and manufacturing effects. Several propellers of the same design were 3D printed with cut-in and add-on serrations. To overcome the influence of laminar to turbulent transition over the blade surface, some propellers also include additional surface roughness to trigger the turbulence. All propellers are tested in an anechoic room, where far-field noise and aerodynamic performances are collected. The results show that serrations are a viable method to control trailing edge noise at low RPM, where broadband noise dominates over tonal noise, and that the add-on serrations with a trip are in better agreement with the theoretical results, thus highlighting the importance of the manufacturing method during the design phase.
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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 | 0.001 | 0.003 |
| 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 | 0.000 | 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".