Large-Eddy Simulation and Aeroacoustic Prediction of Supercritical Airfoil Side-Edge Noise
Bibliographic record
Abstract
Airfoil self-noise is investigated on a cantilever wing with a supercritical profile with a compressible wall-resolved large-eddy simulation. The Reynolds number based on the chord is 620,000, and the angle of attack is 5°. The aerodynamic results reveal the development of a complex vortex system at the side edge, including primary, secondary, and tertiary vortices that govern aerodynamic noise production. The wall-pressure coefficient and root-mean-square pressure coefficient contours highlight the side-edge shear layer and flow impingement of the primary vortex at the pressure-side edge as important noise-generation mechanisms. Dynamic mode decomposition shows that the dominant pressure modes on the airfoil exist along primary and secondary vortex impingement lines. Far-field acoustic predictions based on both solid and porous Ffowcs-Willliams and Hawkings’ analogies demonstrate good agreement with experimental results between 1.5 and 9 kHz. Low-frequency tonal humps observed in the spectra result from duct acoustic modes excited by airfoil self-noise, a consequence of the installation effect. For this configuration, airfoil side-edge noise did not dominate over airfoil surface noise levels, suggesting that at low to moderate angles of attack, airfoil side-edge noise is not a significant contributor to the overall acoustic emissions.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".