Fast and accurate analytical modeling of broadband noise for a low-speed fan
Bibliographic record
Abstract
The broadband noise sources are investigated on an isolated low-speed fan typical of engine cooling systems. Reynolds-averaged Navier-Stokes (RANS) simulations have been performed on a single blade passage for several flow rates at the same rotational speed. The flow structures responsible for the different noise contributions are identified by a systematic analysis of the simulation results. The aeroacoustic noise predictions are based on Amiet's model for rotating sources in free-field. The contribution of the turbulence-interaction noise and the trailing-edge noise are considered by the appropriate isolated blade response and statistical model of the turbulent sources. The flow parameters of the aeroacoustic response and the turbulent models are extracted from the RANS simulations. The radial evolution of the flow parameters for the different flow rates is analyzed and related to the three-dimensional flows in the machine. The acoustic predictions are validated with experimental spectra measured upstream of the fan in a reverberant room. The two considered mechanisms evolve differently with the flow rate. The leading-edge sources are dominant at low flow rate up to the design point and the self-noise becomes dominant at high flow rate for which the secondary flow structures are limited.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".