Influence of angle of attack on airfoil tip noise
Bibliographic record
Abstract
This study investigates the aerodynamic and aeroacoustic characteristics of airfoil tip noise for a supercritical airfoil profile at α = 5 ° and α = 10 ° using a hybrid noise computation approach. Wall-Resolved Large Eddy Simulations (WR-LES) were performed and validated against experimental data from the University of Toronto. The simulations captured a tri-vortex system (TVS) comprising primary, secondary, and tertiary vortices. At α = 10 ° , upstream shifts in the primary vortex trajectory intensified interactions with the airfoil surface, resulting in amplified surface-pressure fluctuations and increased far-field noise levels. Dynamic Mode Decomposition (DMD) pinpoints dominant acoustic sources and their radiation patterns, distinguishing between duct acoustic modes below 1500 Hz and localized sources above this frequency with clear dipole radiation patterns stemming from three noise sources: the leading-edge, trailing-edge, and side-edge noise. The far-field acoustic predictions, computed using the Ffowcs Williams–Hawkings (FW-H) analogy, showed good agreement with experimental results. Both the solid and porous FW-H formulations accurately captured noise levels, with a 3 dB increase in spectral levels observed at α = 10 ° due to enhanced aerodynamic loading and vortex dynamics. Noise decomposition showed a shift in dominant sources: trailing-edge noise prevailed at α = 5 ° , while tip noise, driven by vortex impingement and crossover, dominated at α = 10 ° , highlighting the complex aerodynamic-acoustic interplay.
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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 | 0.001 | 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".