Tunable multi-metamaterials intergrated with auxiliary magnetorheological resonators
Bibliographic record
Abstract
Abstract In recent years, there has been a surge in interest in utilizing multi-metamaterials for various purposes, such as vibration control, noise reduction, and wave manipulation. To enhance their performance and tunability, auxiliary resonators and magnetorheological elastomers (MREs) can be effectively integrated into these structures. This research aims to formulate the wave propagation analysis of periodic architected structures integrated with MRE-based auxiliary resonators. For this purpose, cantilever MRE beams are embedded into conventional unit cells of square and hexagonal shapes. Integrating MREs into multi-metamaterial structures allows for real-time tuning of the material properties, which enables the multi-metamaterial to adapt dynamically to changing conditions. The wave propagation in the proposed architected structures is analyzed using the finite element method and Bloch’s theorem. The studied low-frequency region is significant, and the addition of MRE resonators leads to the formation of a mixture of locally resonant and Bragg-type stop bands, whereas the basic structures (pure square and hexagonal) do not exhibit any specific band gaps in the considered region. The effect of different volume fractions and applied magnetic fields on the wave-attenuation performance is also analyzed. It is shown that band gaps depend on the material parameters of the resonators as well as the applied magnetic flux stimuli. Moreover, the area of band gaps changes, and their operating frequency increases by increasing the magnetic flux around the periodic structure, allowing for the tuning of wave propagation areas and filtering regions using external magnetic fields. The findings of this study could serve as a foundation for designing tunable elastic/acoustic metamaterials using MRE resonators that can filter waves in predefined frequency ranges.
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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".