Improvement of the sound absorption performance of sandwich panel using inhomogeneous micro-perforations
Bibliographic record
Abstract
Sandwich structures are widely used in several fields because of their mechanical performance with low weight. In this paper, a sandwich panel made of a core and a top and a back panel is investigated using the finite element method. The core consists of 33 hexagonal cells and each cell is connected to a micro-perforation. To improve the sound absorption, inhomogeneous micro-perforations are considered within the top panel. It is shown that with homogenous micro-perforations made of the same diameter, the sound absorption coefficient presents only one resonant peak. When 2, 3, and 6 different sets of micro-perforation diameters are considered, the sound absorption coefficient presents, respectively, 2, 3, and 6 resonance peaks where the surface impedance is close to that of the air. When each micro-perforation diameter is different resulting in inhomogeneous distribution of micro-perforations within the top panel, the sound absorption frequency band is widened and the absorption coefficient value increases while without micro-perforations the absorption coefficient of the sandwich panel is zero over the entire frequency range. The studied structure can offer high mechanical stiffness and good sound absorption performance.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".