Damping prediction of highly dissipative meta-structures through a wave finite element methodology
Why this work is in the frame
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Bibliographic record
Abstract
Aiming at accurately predicting the global Damping Loss Factor (DLF) for Highly Dissipative Structures (HDS), the current study uses the Wave Finite Element (WFE) methodology. It starts by deriving the forced responses of a Unit Cell (UC) representative of the periodic meta-structure. Then it computes the DLF of the wave via the power balance. The Bloch expansion is employed. The response to a point force applied to the periodic structure is decomposed in the Brillouin zone, allowing the prediction via integration over the wavespace. The global DLF is derived based on the Power Input Method (PIM). The accuracy of the methodology is demonstrated through several cases from simple panels to complex meta-structures. For HDS, results of General Laminated Model (GLM) is exploited for wave DLF and PIM based on Finite Element Method (FEM) data is provided as reference approach for global DLF. The study discusses the influence of bending waves on the DLF estimation for HDS. A final case study with a meta-structure is also offered. The later consists of a doubly periodic coated sphere in a host rubber, it demonstrates the importance of Bloch modes. • Wave-based methodology is developed by Bloch wave expansion to estimate DLF of HDS. • DLF computation confirms the reduced impact of bending wave on HDS forced response. • Higher Bloch modes are essential for DLF estimation of meta-structure with large UC.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it