Experimental and Analytical Investigation of the Tonal Trailing-Edge Noise Radiated by Low Reynolds Number Aerofoils
Bibliographic record
Abstract
An experimental and analytical study of the tonal trailing-edge noise of a symmetric NACA-0012 aerofoil and of a cambered SD7003 aerofoil has been achieved. It provides a complete experimental database for both aerofoils and improves the understanding of the underlying mechanisms. The analysis stresses the high sensitivity of the tonal noise phenomenon to the flow velocity and the angle of attack. Several regimes of the noise emission are observed depending on the aforementioned parameters. The contributions of the pressure and the suction sides are found to vary with the flow parameters too. A special attention has been paid to the role of the separation bubble in the tonal noise generation. Hot-wire measurements and flow visualization prove that the separation bubble is a necessary condition for the tonal noise production. Moreover, the bubble must be located close enough to the trailing edge. Several tests with small-scale upstream turbulence confirm the existence of the feedback loop. Analytical predictions with a classical trailing-edge noise model show a good agreement with the experimental data; they confirm the cause-to-effect relationship between the wall-pressure fluctuations and the radiated sound. Finally, previously reported works on fans and propellers are shortly re-addressed to show that the tonal noise associated with laminar-boundary-layer instabilities can take place in rotating blade technology.
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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 | 0.001 |
| 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 | 0.001 |
| 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".