Prediction of Noise Sources in Axial Compressor from URANS Simulation
Bibliographic record
Abstract
As part of a larger aeroacoustic study focused on rotor–stator interaction noise, the unsteady flow in a single-stage axial compressor is achieved by a numerical Reynolds-averaged Navier–Stokes simulation. The flowfield is precisely analyzed and assessed by comparing it with available experimental and numerical data. Specifically, the rotor wakes correctly agree with previous laser Doppler anemometry measurements. The unsteady flow, especially the potential effects created by the stator vanes, is then investigated, showing that the stator potential field is responsible for a major secondary tonal noise source on the rotor blades. The rotor wake velocity and turbulent content correspond to the excitations for tonal and broadband noise, respectively. They are greatly influenced by the vane potential effects and, to a lesser extent, by their convection with the mean flow. Two methods of extraction are surveyed along the whole span, showing the need to consider the blockage induced by the stator on the wakes. Finally, the realistic tonal acoustic sources of rotor–stator interaction extracted in time on the stator vane surfaces show a noise mechanism mainly driven by the leading edge and some significant variations from hub to tip.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".