Wind turbine aeroacoustic noise prediction using computational models and comparison to experimental measurements
Bibliographic record
Abstract
Energy from wind turbines has enjoyed a remarkable growth worldwide in the past decades. In Canada, generation capacity has increased dramatically. The issue of noise and wind turbines has become an issue of interest for researchers and acoustics practitioners. For utility scale wind turbines, broadband noise emanating from the trailing edge of the wind turbine blade is a large contributor to the overall noise emission. In order to minimize the noise impact, regulatory bodies often set limits to the noise level observed nearby. Good noise predictive tools are necessary to estimate noise emissions before a wind farm development. These tools are developed in this study from computational fluid dynamics studies such as Large Eddy Simulation (LES) in conjunction with the Ffowcs-Williams and Hawkings (FW–H) acoustic analogy to predict the far field sound. These results are compared to those obtained through the use of semi-empirical prediction models. Validation of these predictive tools are compared with experimental measurements of 2D airfoil self noise and with field measurements obtained at the University of Waterloo Wind Energy Research wind turbine field site. The developed predictive tools show good agreement with the measured experimental data leading to further development of the predictive tools.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".