Evaluation of Acoustic Frequency Methods Coupled to Blade Element Momentum Theory 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 far-field harmonic noise.They range in complexity from a direct implementation of the equations derived by Gutin and Deming to Hanson's helicoidal surface theory of propellers.The advantage of these acoustic models is that they do not require chord-wise aerodynamic data and therefore do not need to be coupled to a panel or grid-based aerodynamic solver.Each implemented method is compared to fourteen test cases originating from nine separate published acoustic experiments.The experimental data considered encapsulates a range of propeller geometries, blade numbers, microphone locations, tip speeds, and forward Mach speeds.The implemented acoustic models demonstrate good agreement with the experimental data, particularly for the prediction of the maximum tonal noise for which the model based on Hanson's work has an average error of 7.2 dB.The presented results suggest that the implemented acoustic methods and, in particular, the model based on Hanson's work, remain a valuable resource for propeller noise prediction, especially for design and optimization studies, where a low runtime is important.viii 5.7 Blade passing tone directivities -test case 10. . . . . . . . . . . . . .5.8 Tone directivities -test case 11. . . . . . . . . . . . . . . . . . . . . .5.9 Blade passing tone directivities -test cases 12 and 13. . . . . . . . . .5.10 Noise spectra -test case 14. . . . . . . . . . . .
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".