Numerical analysis of honeycomb structure with embedded membrane For tranmission loss improvement
Bibliographic record
Abstract
Honeycomb structures find use in many applications because of their high stiffness to weight ratio and excellent mechanicalimpact energy absorption. However, their acoustic performance is poor. Li et al [1] studied the transmission loss (TL) of lightweight multilayer honeycomb membrane-type acoustic metamaterials experimentally and observed that the sandwich panel acoustic metamaterials exhibit good TL. A lightweight and yet sound-proof honeycomb acoustic metamaterial is investigated by Sui et al. [2] and Lu et al. [3]. The metamaterial structure is made of a lightweight flexible rubber material layer sandwiched between two layers of honeycomb cell plates. They demontrated excellent TL with minimum weight penalty. Li et al. [4] presented a theoretical model to estimate the TL of acoustic micro-membranes, which demonstrate improved TL at low frequency. In this study, the transmission loss of a honeycomb structure with embedded membranes is investigated using the finite element method. It is shown that the transmission loss of the honeycomb structure is significantly improved by the embedded membrane while the TL of the honeycomb structure core alone is zero. The influence of the honeycomb structure cell size is illustrated as well as the effect on the membrane material properties. The investigated structure presents good TL especially at low frequencies.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".