Application Study on a Flexible Rebound-Type Acoustic Metamaterial at Low Frequency
Bibliographic record
Abstract
A flexible rebound-type acoustic metamaterial with high sound transmission loss (STL) at low frequency is proposed, which is composed of a flexible, light-weight membrane material and a sheet material - Ethylene Vinyl Acetate Copolymer (EVA) with uneven distributed circular holes. STL was analyzed by using both computer aided engineering (CAE) calculations and experimental verifications, which depict good results in the consistency between each other. An obvious sound insulation peak exists in the low frequency band, and the STL peak mechanism is the rebound-effect of the membrane surface, which is proved through finite element analysis (FEA) under single frequency excitation. Then the variation of the STL peak is studied by changing the structure parameters and material parameters of the metamaterial, providing a method to design the metamaterial with high sound insulation in a specified frequency range. A group of combined metamaterial is proposed which consist of the metamaterial and felt or PU material, which can avoid some of the resonance frequency points and fill the STL valleys of the pure metamaterial structure. It could achieve light-weight and flexible design in the field of low frequency vibration and noise reduction, which will play a promoting role to realize engineering applications in automotive, aircraft, submarines and other industries.
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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".