Improvement of micro-perforated panel absorbers sound performance using resistive screens and sensitivity analysis of the composite absorber models
Bibliographic record
Abstract
The sound attenuation performance of a composite absorber constituted by a micro- perforated panel, a resistive screen that is inserted into the air cavity and a rigid back plate, is studied. Using a nonlinear acoustic impedance model, the surface impedance of the absorber is determined by transfer matrix method, and the models are validated by comparison with experimental measurements performed at different sound pressure levels up to 150 dB. The contribution of the resistive screens in improving the acoustic performances of micro-perforated panel absorbers is presented. It is shown that the resistive screen increases the resistance of the absorber resulting in broadening of the absorption frequency band and improvement of the absorption coefficient when an appropriate perforation ratio of the panel is used. Sensitivity analysis is performed at higher pressure excitations, and the dimensionless input parameters are the perforation ratio of the panel, the ratio of the perforation diameter by the thickness of the panel, the ratio of the cavity depth by the thickness, the orifice Mach number and the resistance per unit area of the screen. The analysis shows the impact of the input parameters on the normalized surface impedance and the sound absorption coefficient of the absorber. It is demonstrated that the resistance per unit area of the screen influences strongly the acoustic properties of the absorber in the linear regime, while the perforation ratio of the panel and the orifice Mach number are the dominant parameters which control the acoustic behavior of the absorber in the nonlinear regime.
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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 | 0.001 |
| 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 | 0.000 |
| 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".