The Application of a MEMS Microphone Phased Array to Aeroacoustics of Small Wind Turbines
Bibliographic record
Abstract
A low-cost microelectromechanical (MEMS) microphone array was developed to investigate and localize aeroacoustic sources. A 1 m by 1 m array was designed as a portable measurement apparatus capable of locating noise sources from small rotating wind turbines in conjunction with open-jet wind tunnels or field measurements. Beamforming algorithms were implemented to allow simulation of sound sources and conditions expected to be encountered in testing. Array testing with a known monopole source, multiple sources and different frequencies located the mainlobes accurately at a number of frequencies and distances. A rotating sound source was located spatially and tracked using conventional beamforming. Experimental results from the acoustic testing of a 1.3 metre rotor diameter wind turbine in an open-jet wind tunnel indicated strong evidence of trailing edge noise at freestream velocities of 4.5 m/s and 5.5 m/s. Source maps for the 5.5 m/s tests reveal that the turbine's aeroacoustic emissions are most prevalent at the outer portions of the radius, but not necessarily at the tip. The azimuthal location of the greatest sound pressure levels was found to be roughly 120° to 130°, measured from the upward vertical, for most test conditions. An analytical model confirmed this sector to be the approximate expected azimuthal location of the source in the time-averaged source maps.
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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".