Wind Tunnel Testing to Evaluate Noise Emissions from a Small Wind Turbine
Bibliographic record
Abstract
The wind power industry has experienced fast and tremendous growth, globally, in recent years.Consequently, there is a growing interest in investigating aerodynamics and aeroacoustics characteristics of wind turbines and developing environmental standards aimed at decreasing noise levels in the design and manufacture of these machines.This study was carried out in the Carleton University Wind Induced Dynamic Laboratory (WInDLab) that was designed to evaluate and quantify noise levels emitted by a small horizontal axis wind turbine.This investigation provided the firstever set of measurements carried out in the WInDLab following the successful installation of the wind turbine.To achieve the objective of this project, first, the characterization of the wind tunnel had to be carried out.The wind velocity profile of the wind tunnel was characterized and results indicated that its profile is not perfectly symmetrical with respect to the tunnel centreline.The background noise levels of the WInDLab wind tunnel for different fan RPMs were recorded with a Brüel & Kjær (B&K) Prepolarized Free-field Type 4189, ½" (1.27 cm) microphone, which was located at six positions in line with IEC61400-11 regulations.The noise measurements obtained were post-processed using the MATLAB software application.Plots for the sound pressure level (SPL) vs frequency of the wind turbine noise spectra, as well as the background noise level at 500 RPM, 575 RPM and 650 RPM of the wind tunnel fans were derived.iii The background acoustic data obtained for the wind tunnel showed that the wind tunnel is not quiet enough for measurements of the type undertaken.The wind tunnel would need to be silenced and acoustically treated to capture noise with low frequencies, so as to attain a signal ratio of at least 10 dB between the measured signal and the background noise of the wind tunnel across all frequencies.Recommendations for further work have been provided.
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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 | 0.001 |
| 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) | 0.002 | 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".