Computational aeroacoustic prediction of trailing edge noise for small wind turbines
Bibliographic record
Abstract
Abstract The study of aeroacoustic noise generated by small wind turbines is important to increase acceptance and implementation of the technology. Small wind turbines have unique challenges due to the low Reynolds number (Re) flow the blades experience, which introduces a potential for tonal noise. Computational aeroacoustics can be applied during the design stage of the turbine blades to improve acoustic performance. This work aims to validate a fully analytical aeroacoustic model by analyzing a SD 7037 blade segment at static angles of attack, with comparison to experimental flow and acoustic data. The Ffowcs-Williams and Hawkings (FW-H) acoustic model is used in combination with Large Eddy Simulation (LES). The following simulation parameters were examined: mesh quality, mesh density, inlet turbulence and spanwise boundary condition. These parameters change the boundary layer (BL) transition process and the formation of the laminar separation bubble on the suction side of the blade segment, both of which impact the aeroacoustic noise prediction. It was found that improvement in mesh quality and density on the surface of the blade segment resulted in improved BL simulation and tonal noise prediction. Alteration of the inlet turbulence and spanwise boundary condition did not have as large of an effect.
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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) | 0.001 | 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".