Phased microphone array measurements of a bombardier aircraft scaled model
Bibliographic record
Abstract
Aerodynamic testing of high fidelity models is a standard practice for aircraft manufacturers. With the development of phased microphone array technology, aeroacoustics can become an integral part of the design process of an airplane. In 2012, the National Research Council Canada and Bombardier Aerospace carried out a collaborative research program focusing on measuring the sound generated by a high-fidelity aerodynamic wind tunnel model. The noise of various configurations was measured at combinations of three wind speeds and three microphone array polar orientations above the model. Several calibration reference noise waveforms were explored to determine the most effective in a highly reverberant hard-walled test section. The calibration data analysis showed that meaningful beamform maps above 30kHz could be generated when a pulsed sine wave was applied as the reference noise signal in the calibration process. For the model configurations tested, slat noise dominated the sound maps processed using either conventional beamforming or CLEAN-PSF. The wing flap edge noise as well as the contributions from the main gear were also discernible. For each configuration, one data point was taken while the microphone array was rotating instead of stationary. The computationally intense post-processing procedure for these data provided a detailed map of the sources due to reduced spatial aliasing. The sound levels measured with the rotating array were at a slightly lower magnitude owing to the averaging of the source over its full directivity pattern. Future work includes implementing a calibration procedure for the rotating array and improving the signal-to-noise ratio of the signals so that shorter microphone time histories can be used to extract directivity information from the datasets of the rotating array.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".