Aeroacoustic Optimization of Flat-Plate Serrated Trailing Edge Extensions for Broadband Noise Reduction
Bibliographic record
Abstract
The ability of trailing edge serrations to reduce turbulent boundary layer trailing edge noise is examined through numerical optimization studies.The in-house optimization program, SAGRGSTEN, is developed and implemented.Two different serration geometries are optimized for both, the overall noise (from 20 Hz to 20 kHz), and the noise produced at individual frequencies throughout the same range.The noise was modeled using Howe's semi-empirical model for a semi-infinite flat plate, at zero angle of attack to the mean, low Mach number, flow.Results of the optimization studies are used to investigate the influence of serration design parameters.It is shown that the multi-tooth-size serrated trailing edges yield greater noise reductions than single-size serrated trailing edges.Based on this finding, a novel design is proposed for a multi-tooth-size sawtooth serrated TE profile.It is also shown that, based on the bounds used, the serration design that yields the greatest amount of total noise reduction is a multi-tooth-size slitted trailing edge.iii To Rachel, thank you for always believing in me.resources, and patience have been greatly appreciated.Thank you for encouraging me to achieve more, always work harder, and maintain perspective.
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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) | 0.001 | 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".