Dynamic analysis of stepped functionally graded piezoelectric plate with general boundary conditions
Bibliographic record
Abstract
Abstract A stepped functionally graded piezoelectric (FGPM) plate model is proposed for the first time, and its free and forced vibration are studied by using the domain energy decomposition method. The segmentation technique is used to discretize the structure along the length direction. At the structural boundary and piecewise interface, the weight parameters are introduced to satisfy the boundary conditions and the coordination conditions between the piecewise interfaces. On this basis, the boundary conditions of subdomains can be regarded as free boundary a constraint, which reduces the difficulty in constructing the displacement admissible function. Because all the structures of subdomains are the same, the displacement admissible functions of them are uniformly obtained by the two-dimensional Jacobian orthogonal polynomial expansion. The potential energy function of the plate is based on the first-order shear deformation theory. The displacement admissible function is substituted into the potential energy function, then the standard variational operation is used to obtain the solution equation of the dynamic characteristics of the FGPM plate. Through the numerical calculation, the superior calculation performance of the method is proved, and it is not limited to the boundary conditions. On this basis, the effects of geometric and material parameters on free and forced vibration of FGPM plate are also discussed.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".