Experimental and Computational Characterization of Canonical Side-Edge Noise
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-4481.vid Airfoil self-noise is investigated on a canonical cantilever wing with a supercritical profile (2% Camber, 13% thickness) with a compressible wall-resolved large-eddy simulation (WR-LES). The study focuses on the side-edge flow structures responsible for noise generation and provides detailed comparisons of surface flow visualization and far-field acoustics between simulation and experiment. 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 to be important noise generation mechanisms. Dynamic Mode Decomposition (DMD) analysis identifies the dominant acoustic modes and source locations. The solid surface Ffowcs Williams and Hawkings (FW-H) analogy is used to compute the far-field noise levels. This study provides insights into the aerodynamic and acoustic coupling of the airfoil and contributes to the understanding of tip-noise sources for supercritical airfoils.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".