Influence of Swept Blades on Low-Order Acoustic Prediction for Axial Fans
Bibliographic record
Abstract
The low-speed fans used for automotive engine cooling contribute to a significant part of the global noise emitted by the vehicle. A low-order sound-prediction methodology is developed considering the blade sweep-angle effect on the acoustic predictions of the turbulence-impingement and the trailing-edge noise-generating mechanisms. We modeled these through the application of a semianalytical method based on Amiet’s airfoil theory, appropriately adapted via a strip-theory approach accounting for rotation and modified to include the blades forward curvature. Sweep was already shown in the literature to reduce the noise emitted by isolated airfoils, but its effect on rotating machines was not yet well understood. In this study, we show that the effect of the sweep-angle is to globally reduce the emitted noise by the fan and to change the sound distribution of the sources along the blade span. Thus, the sweep-angle must be considered not only because it yields a better comparison with experimental results but also because wrong conclusions on the dominating noise-generating mechanisms can be drawn when this effect is not taken into account. The investigation is finally complemented by a sensitivity analysis focusing on some of the key parameters characterizing the acoustic prediction.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".