Nonlinear free vibration analysis of sandwich shell structures with a constrained electrorheological fluid layer
Bibliographic record
Abstract
Nonlinear vibration analysis of sandwich shell structures with a constrained electrorheological (ER) fluid is investigated for different boundary conditions. To accomplish this, a nonlinear finite element model of a multi-layer shell structure with an ER fluid layer in the core of the sandwich shell has been developed. A new notation referred to as H-notation is presented, in place of the two well known notations referred to as B- and N-notation, in order to represent the nonlinear equations of motion. This notation leads to a considerable reduction in the computational costs caused by the time-consuming integrations in the nonlinear vibration analysis of the structure using a direct iteration technique. In particular, this notation is very useful for solving the nonlinear vibration analysis of sandwich layered shell structures in which large numbers of integrations are required to be repeatedly performed throughout the direct iteration technique. Finally, for different boundary conditions, the effects of small and large displacements, core thickness ratio and electric field intensity on the nonlinear vibration damping behavior of the sandwich shell structure are presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".