Bibliographic record
Abstract
The excitation of acoustic resonance by flow over a rectangular cavity can generate acute noise, cause damage to equipment, and interrupt operation. In this work, a passive control technique to suppress the excitation of acoustic resonance by the flow over rectangular cavities is experimentally investigated. A span-wise rod that generates high-frequency vortices is mounted upstream of the cavity leading edge to prevent the flapping of the shear layer. The effect of the rod parameters on the mechanism of acoustic resonance suppression is identified by means of acoustic pressure and particle image velocimetry (PIV) measurements. It is found that the effectiveness of this control technique is significantly dependent on the streamwise location of the rod with respect to the cavity leading edge, the gap between the rod and the wind tunnel wall, and the cavity aspect ratio. In addition, PIV measurements revealed that, in effective rod configurations, the vortices generated in the gap between the control rod and the wall alter the development of the shear layer. Moreover, analysis of the Reynolds stresses showed that fluctuations in the wake of the rod prevent the shear layer from impinging on the cavity downstream edge. Consequently, this interaction interrupts the initiation of the feedback mechanism responsible for the onset of acoustic resonance excitation. Finally, a universal criterion is developed to predict an optimum region to implement the control rod upstream of the cavity leading edge to effectively suppress the acoustic resonance excitation.
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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) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.216 | 0.444 |
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".