A Modification to Logarithmic Spiral Beamforming Arrays for Aeroacoustic Applications
Bibliographic record
Abstract
Acoustic beamforming is an experimental tool that can be used to locate and quantify aeroacoustic noise sources. Much of the available aeroacoustic beamforming literature presents beamforming results of noise at relatively high frequencies. There are few experimental acoustic beamforming results for acoustic frequencies between 1 kHz and 5 kHz, although much of the literature for airfoil self noise at low to moderate Reynolds number fits in this frequency range. One difficulty with acoustic beamforming of relatively low frequency noise is the large size of the main lobe in the beamformer output, resulting in a potential inability to resolve acoustic sources within close proximity to each other. This paper provides a detailed comparison between grid, randomized, logarithmic spiral and modified logarithmic spiral arrays and a discussion of the performance of each array type over a range of low frequencies (1 kHz-5 kHz). The spiral arrays were found to have lower sidelobe levels over a wider frequency range than the grid and random array. For frequencies less than 4.84 kHz and greater than 12.36 kHz, the modified logarithmic spiral exhibited smaller sidelobe magnitudes than the unmodified logarithmic spiral. An experimental verification of a modified logarithmic spiral using a small headphone in an anechoic environment is also provided. This showed that the error of the measured noise source locations placed 600 mm from the array plane is within 20 mm for the series of locations studied. The estimation error was shown to be dependent on the source location and direction from the center of the 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".