Analytical model for predicting rotor-stator interaction tonal noise in low-speed axial fans
Bibliographic record
Abstract
Analytical models for aeroacoustic simulations are valuable due to their potential to reduce computational costs, a crucial consideration to optimize noise performance. Analytical models proposed so far focus mainly on isolated steady and unsteady fan tonal noise, often neglecting the significant contribution of rotor-stator interaction. As a matter of fact, for short axial spacing, tonal noise from potential flow interactions is relevant. This study presents an analytical model for predicting rotor-stator interaction noise caused by the stator vane potential effect on blades. The model requires only time-averaged flow fields from URANS simulations with periodic boundary conditions. Key features include accounting for uneven spacing and arbitrary chord, twist, and sweep distributions of blades and vanes. The model is based on the Sears aerodynamic model for blade loading fluctuations and the time-domain Ffowcs Williams-Hawkings formulation for noise prediction. Validations with evenly and randomly spaced blade show the model capability to predict tonal noise trends. The model captures noise at the blade passing frequency and harmonics associated with rotor-stator interaction. The model fails to predict noise at higher harmonics due to trailing edge noise sources, not currently accounted for. Future works will extend the model to broadband noise sources.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".