Evaluation of Acoustic Frequency Methods for the Prediction of Propeller Noise
Bibliographic record
Abstract
The accuracy of several computationally inexpensive acoustic frequency methods is evaluated across a range of propeller geometries and operational conditions. The acoustic models considered predict both near-field and far-field harmonic noise. The implemented models approximate or ignore chordwise noncompactness such that they do not require chordwise aerodynamic data, and therefore do not need to be coupled to a panel or grid-based aerodynamic solver. Each implemented method is compared to 14 test cases originating from nine separate published acoustic experiments. The experimental data considered encapsulate a range of propeller geometries, blade numbers, microphone locations, tip speeds, and forward Mach speeds. The implemented acoustic models demonstrate reasonable agreement with the experimental data, particularly for the prediction of the maximum tonal noise for which Hanson’s model showed the greatest overall accuracy with an average error of 5.9 dB. Using different prediction models based on the freestream velocity reduces the error to 4.7 dB. The presented results suggest that the implemented acoustic methods remain a valuable resource for propeller noise prediction, especially for design and optimization studies, in which a low runtime is important.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".