A Data-Driven Method for Stall Noise Predictions
Bibliographic record
Abstract
This paper presents a data-driven low-cost stall noise model based on the Brooks, Pope and Marcollini broadband noise model. This model is composed of a machine-learning enhanced BPM component, and a log-linear stall noise component. Using an adjoint-based inversion process, ideal tuning parameters are found for the model for multiple flow conditions and airfoil geometries. A neural network is trained on geometry and flow condition information, in addition to the ideal tuning parameters, such that near-ideal tuning parameters can be predicted. The model is bench-marked against the unmodified Brooks, Pope and Marcollini model for test cases outside of the training dataset. It is found that the stall noise model predicts stall noise within a range of ±10 dB and is significantly better at predicting the sound pressure level of both stalled and unstalled airfoils than the unmodified Brooks, Pope and Marcollini 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 | 0.001 | 0.002 |
| 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 | 0.001 |
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".