Numerical investigation on 2D metamaterial under normal incidence
Bibliographic record
Abstract
Investigations in solid-state physics show that metamaterials, a kind of periodic material, can produce a band of frequency gap. When a sonic wave’s frequency falls into this frequency gap, it cannot propagate. Enlightened by the concept of frequency gap, researchers recently investigated various metamaterials. The search for bandgap(s) is critical in such innovations. The present letter presents the analysis of the band of frequency gap(s) for two and three-component systems as 2D period materials using the Finite Element Method. The investigation highlights the influence of geometrical parameters on the bandgap. Modelled metamaterial uses a core of aluminium, a coating of natural rubber and a matrix of air. For the lattice constant’s specified value, with the core’s increasing size, the bandgap shows to increase. The general effect of the coating leads to the flattening of the dispersion curve. For the square lattice and circular core, the bandgap appears around a net core size (with and without coating) of around 30%–32% of the lattice constant. Multiple frequency bandgaps appear of substantial sizes at the core’s threshold size, that is, when the core’s net radius is equal to half of the lattice constant. The softer material coating is found as a potential alternative to tune and control acoustic metamaterials. With a three-component system, bandgaps appear comparatively at significantly lower frequencies. Lower edge frequencies for such bandgaps are found to be independent of the core and coating size.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".