Finite Element Analysis of Honeycomb Membrane-Type Acoustic Metamaterial
Bibliographic record
Abstract
In this paper, a honeycomb membrane-type acoustic metamaterial made of a honeycomb structure with embedded membrane layers is investigated using the finite element method. This lightweight material presents excellent transmission loss (TL) at low frequency. The impacts of the membrane material properties and of the honeycomb cell size on the transmission loss are studied numerically. It is shown that the TL increases over a large frequency band when the honeycomb cell size decreases while the displacement magnitude of the membrane is reduced and the mode shape is affected. The influence of the membrane thickness on the TL and displacement magnitude and mode shape at different frequencies is presented. It is observed that the TL presents multiple resonant peaks as the thickness of the membranes is reduced. An improvement of the TL is observed around the anti-resonant frequencies by increasing the damping loss factor of the membrane, which causes a reduction of the resonant displacement magnitude and TL peaks amplitude. Honeycomb structures with two and three embedded membrane layers are analyzed numerically and the effects of the number of membrane layers and of the thickness of the air gap between membranes are illustrated. The investigated metamaterial can be useful in many noise control engineering applications.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".