Topology optimization of damping layer in frequency-dependent viscoelastic sandwich panels considering steady-state free vibration
Bibliographic record
Abstract
Attaching viscoelastic materials (VEMs) to structures has been widely used to improve structural dynamic properties. Nevertheless, the adhesion of VEMs inevitably leads to the increase in overall structural mass. Topology optimization is one of the most effective methods to tackle this contradiction. In the traditional topology optimization, VEMs damping cores in constrain layer damping (CLD) sandwich structures are often simplified into linear elastic materials with constant parameters, usually ignoring their frequency-dependent and temperature-dependent dynamic characteristics, which will cause the distortion of the optimization results. Therefore, we propose a topology optimization method for CLD sandwich panels with a frequency-dependent VEM damping core considering steady-state free vibration in this study, where the anelastic displacement fields (ADF) model is adopted to combine the derived frequency-dependent complex constitutive relationship with the layerwise finite element (LFE) model, and the iterative modal strain energy (IMSE) method is used to determine the modal parameters of the CLD sandwich panels. The results of numerical examples show that the proposed method in this study not only has the advantages of simple and intuitive model, high calculation efficiency, and accuracy but also can achieve relatively good dynamic properties of the CLD sandwich structures.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".