Vibration analysis and design optimization of sandwich cylindrical panels fully and partially treated with electrorheological fluid materials
Bibliographic record
Abstract
Compared with viscoelastic materials, electrorheological fluids can be effectively used to suppress the vibration over a broad frequency and temperature range. In this study, vibration analysis and damping characteristics of sandwich cylindrical panel structures using semiactive electrorheological fluid treatments have been investigated for different boundary conditions. Unconstrained viscoelastic material has been used at boundary and untreated locations to seal electrorheological fluids. First, an efficient finite element method has been formulated to investigate the effect of electric field intensity and thickness of top constrained elastic layer on the vibration and damping performance of the viscoelastic- and electrorheological-based sandwich cylindrical panel. Then, a design optimization methodology has been developed to simultaneously optimize the number of unconstrained viscoelastic and constrained electrorheological fluid patches and their distributions, thickness ratios of the electrorheological core and constrained elastic layers to base layers, and the external electric field intensity. The methodology integrates the finite element model of the sandwich panel with the combined genetic algorithm and sequential quadratic programming to effectively identify the global optimal solutions. The results show that for some boundary conditions, the sandwich panel partially treated with electrorheological fluids provides better damping performance compared with that of fully treated.
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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".