A study on minimizing the noise from a trailing edge by the use of optimized serrations
Bibliographic record
Abstract
An optimization study is presented in which the noise produced by flow over the trailing edge of a flat plate is minimized via serrations. The theoretical optimum configuration is found by changing the geometry of the serrations with a non-linear gradient based algorithm. The theoretical noise produced by the trailing edge is determined using the semi-empirical model developed by Howe. As expected, the configuration of the trailing edge serrations that produced the least noise over all frequencies (from 20 Hz to 20 kHz) was found to be those with the smallest width (approaching zero) and largest root-to-tip distance (up to the assigned upper-bound). The optimum serration geometry for individual frequencies was found to be highly dependent on the upper bound given to the height. In addition to the optimization study, an analysis is presented of a method for modifying the noise model to capture the high-frequency noise increase (relative to a straight trailing edge) that is regularly observed in experiments. Results are presented for model validation by a comparison to the sound pressure level frequency spectrum produced experimentally.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".