Tonal Noise Prediction Of Serrated Trailing-Edge Airfoils
Bibliographic record
Abstract
In this work, Embedded Large Eddy Simulations are employed in tandem with the Ffowcs Williams-Hawkings model to predict the tonal peaks of NACA0012 airfoils having different noise-suppressing trailing-edge configurations. Different non-flat plate sawtooth serration con-figurations are investigated and experimental wind tunnel testing is performed. Results are validated then compared with experimental measurements, and qualitative agreement is obtained in terms of flow statistics and the far-field noise spectra. TE sawtooth serrations are shown to significantly modify the aerodynamics of the wake and improve mixing across the airfoil. Experimental results confirm that sawtooth serrations reduce the broadband noise radiated by the airfoil at the expense of generating a tonal peak, caused by vortex shedding associated with the bluntness of the serration roots. Longer serrations, and higher values of root bluntness are responsible for the strength of the shed vortices and the intensity of the radiated tonal noise. The frequency at which the tonal peaks occur can be controlled for the same serration amplitude and root bluntness by modifying the wavelength. Larger wavelength values for the same serration amplitude and root bluntness lead to higher tonal peak frequencies, and larger values of root bluntness for the same serration amplitude and wavelength lead to lower tonal peak frequencies accompanied by higher peak amplitudes.
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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 | 0.000 |
| 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".