Numerical Prediction of Tonal Aeroacoustic Noise Produced by Small Wind Turbines
Bibliographic record
Abstract
Abstract The aeroacoustic design of small wind turbines (SWTs) can be challenging due to the possibility of the low Reynolds number (Re) flow over the blades generating tonal noise. The numerical prediction of this tonal noise using computational aeroacoustics can improve the understanding of the flow mechanisms behind the tonal noise to improve SWT blade design. In this study, wall-resolved incompressible large eddy simulation (LES) and the Ffowcs-Williams and Hawkings (FW-H) acoustic analogy are applied to a low Re airfoil, SD 7037, at Re = 4.1 × 104 to assess the ability of this method to predict tonal noise. The tonal prediction at 1° angle of attack aligned with experimental measurements and further analysis confirmed that the Kelvin-Helmholtz (K-H) rolls in the suction side laminar separation bubble (LSB) are the source of the aeroacoustic tone. The tone is due to the K-H rolls passing the trailing edge of the airfoil, and a secondary tone intermittently appears due to a 3D instability in the K-H roll. The accurate prediction of tonal noise using LES and FW-H opens the possibility of incorporating this method into the aeroacoustic design of low Re airfoils used for SWTs.
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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 | 0.001 |
| 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".