Bandgap merging and widening of elastic metamaterial with heterogeneous resonator
Bibliographic record
Abstract
Abstract Much effort has been devoted to exploring broad bandgap in low frequency with limited mass by various structural designs of elastic metamaterials (EMs). In this paper, a heterogeneous resonator configuration in 1-D dissipative lattice mass system without additional increase of mass and coupling complexity is presented to generate multiple bandgaps. Special attention is focused on the effect of parameters control to merge the multiple bandgaps, and the analytical expression of parameters setting is obtained for broad bandgap merging. Specifically, the merging of all bandgaps in EMs with two heterogeneous resonators achieves 30% lower and 135% wider bandgap than that of classical local resonance (LR) EMs with the same mass ratio and stiffness ratio, while the merging of three LR bandgaps in EMs with three heterogeneous resonators is 20% lower and 63% wider. The relative movements of masses in the unit cell at different frequencies are depicted to reveal the working mechanism. The effect of damping factor on broadening vibration attenuation regime is studied. Finally, the vibration transmission properties of finite system with heterogeneous resonators, mass graded resonators and mass detuning resonators are compared to ensure the broad vibration attenuation range. This work could be beneficial for the design of vibration attenuation application in industry.
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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 | 0.001 |
| 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".