Granular core architecture for vibration control: An innovative dissipative approach through inter-particle shear in composite beams
Bibliographic record
Abstract
Controlling structural vibrations remains a major engineering challenge, particularly for applications requiring efficient energy dissipation. While traditional solutions often rely on viscoelastic multilayers, this study introduces an innovative architected beam exploiting a granular medium in shear, where interparticle friction serves as a dissipative mechanism. To adapt this concept to flexural wave control, a composite beam with a granular core was designed. This device confines a granular medium between two beams, with in-phase flexural movements inducing shear in the granular core, thus activating energy dissipation. A nonlinear homogenized model of a three-layer beam was developed, incorporating a previously established granular behavior law. The vibration attenuation performance was compared to that of conventional viscoelastic multilayer systems. Results demonstrate that the granular architecture offers significant energy dissipation through particle shear, outperforming traditional methods in certain frequency ranges. The study also proposes pathways for experimental implementation, with potential applications in fields requiring high-performance vibration control, such as aerospace or civil engineering. This work opens new perspectives in metamaterial design by combining granular mechanics and structural dynamics for customized vibration attenuation.
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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) | 0.000 | 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".