Wave-based approaches for wavespace of highly contrasted structures with viscoelastic damping
Bibliographic record
Abstract
The present study investigates the wavespace of Highly Contrasted Structures (HCS) and Highly Dissipative Structures (HDS) by wave-based models. The Asymptotic Homogenization Method (AHM), exploits the asymptotic Zig-Zag model and homogenization technique to compute the bending wavenumbers via a 6th-order equation. The General Laminate Model (GLM) employs Mindlin’s displacement field to establish displacement-constraint relationships and resolves a quadratic Eigenvalue Problem (EVP) of the dispersion relation. The Wave Finite Element (WFE) scheme formulates the Nonlinear Eigenvalue Problem (NEP) for waves in varying directions and tracks complex wavenumbers using Weighted Wave Assurance Criteria (WWAC). Two approaches are introduced to estimate the Damping Loss Factor (DLF) of HDS, with the average DLF calculated by the modal density at various angles where non-homogeneity is present. Evaluation of robustness and accuracy is made by comparing the wavenumbers and DLF obtained from AHM and GLM with WFE. WFE is finally extended to a sandwich metastructure with a non-homogeneous core, and the Power Input Method (PIM) with Finite Element Method (FEM) data is employed to assess the average DLF, demonstrating an enhanced DLF compared to layered configurations with the same material portion, indicating increased energy dissipation due to the bending-shear coupling effects.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".