Acoustic Response of Multiple Shallow Cavities and Prediction of Self-Excited Acoustic Oscillations
Bibliographic record
Abstract
Self-sustaining oscillations of flow over ducted cavities and in corrugated pipes are a known source of tonal noise and excessive vibration in industrial applications. Corrugated pipes can be modeled as a series of axisymmetric cavities. In the current study, the aero-acoustic sources generated by one-, two-, and three-cavity configurations have been experimentally investigated by means of the standing wave method (SWM) for a wide range of Strouhal numbers and acoustic excitation levels. The source strength is found to increase in a nonlinear manner with increasing the number of cavities. Moreover, the self-excited acoustic resonances of the same cavity combinations are investigated. The source characteristics are compared with the observed lock-in range from the self-excited experiments. A prediction model is also developed to utilize the measured source characteristics for estimating the amplitude of the cavities self-sustained oscillations. The self-excited experimental data are used to assess the effect of acoustic absorption at the cavity edges. This absorption is found to be substantial and must be accounted for in the prediction model. When the model is supplemented with appropriate loss coefficients, it predicts fairly well the pulsation amplitude within the resonance lock-in range of the studied multiple cavity configurations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".