Evaluation of a Cascade-Based Acoustic Model for Fan Tonal Noise Prediction
Bibliographic record
Abstract
This study aims at evaluating an analytical model for the prediction of tonal fan noise created by the rotor–stator interaction. The methodology consists of comparing unsteady flow simulations with the cascade-based acoustic model to quantify the influence of some technological effects not included in the model. The simulations are performed with a dedicated turbomachinery flow solver Turb’Flow on a simplified stator vane cascade. They allow discussing the effects of both model assumptions of no vane thickness and inviscid flow on the predictions of the acoustic sources as well as on the modal acoustic powers radiated within the duct. The Kutta condition is found to be efficient to locally represent the viscosity effects, and the vane thickness tends to moderately modify the distributions of the sources and the amplitudes of the duct modes. Moreover, a three-dimensional decomposition of the aerodynamic excitation is proposed and coupled with the three-dimensional analytical cascade response. This fully three-dimensional acoustic model is evaluated by comparisons with an unsteady simulation of a realistic axial compressor stage. An improvement in the prediction of the acoustic sources and powers is clearly shown with respect to the previously used two-dimensional version of the model.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".