Optimization and Testing of Flat-Plate Trailing-Edge Serration Geometry for Reducing Airfoil Self-Noise
Bibliographic record
Abstract
With an expanding network of transportation and the use of air traffic, noise radiated from civil aircraft during takeoff and landing have become a major concern to communities nearby airports. The objective of this work is to investigate the reduction of turbulent boundary layer-trailing edge (TBL-TE) interaction noise. In order to test the concept of noise reduction, the required experimental setup is designed and created. The first part of this study focuses on developing an aeroacoustic wind tunnel test section at Carleton University. The test section walls have been acoustically treated to simulate an acoustically far-field environment with forwarding flight. The two sides of the wind tunnel test section are fitted with anechoic chambers and lined with acoustic transparency tensioned cloth screens which act as an interface between the test section and the anechoic chambers to provide a smooth flow surface while eliminating the need for a jet catcher and reducing interference effects. The ability of the trailing edge serrations to reduce TBL-TE noise is first analyzed through numerical optimization study, and trailing edges are after tested in a wind tunnel. Three different serration geometries are investigated. The noise spectra were modelled using Howe semi-empirical model for a semi-infinite flat plate, at zero angles of attack and low Mach number. NACA 0012 airfoil and flat-plate trailing edges are analyzed and tested. The results of the optimization studies are used to examine the influence of serration design parameters. It is shown that the sawtooth serrated trailing edges yield greater noise reductions than slitted and sinusoidal serrated trailing edges. Experimental and optimization results are then compared. It is concluded that numerical and experimental results are in agreement showing that optimized serrated trailing edge configurations can yield less TBL-TE noise compared to the traditional straight-trailing edge configuration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".