On perturbation method in mechanical, thermal and thermo‐mechanical loadings of plates: cylindrical bending of FG plates
Bibliographic record
Abstract
Abstract The performance of perturbation method in nonlinear analyses of plates subjected to mechanical, thermal, and thermo‐mechanical loadings is investigated. To this end, cylindrical bending of FG plates with clamped and simply‐supported edges is considered. The governing equations of Mindlin's first‐order shear deformation theory with von Kármán's geometric nonlinearity are solved using one‐ and two‐parameter perturbation methods and the results are compared with the results of an analytical solution. The material properties are assumed to vary continuously through the thickness of the plate according to a power‐law distribution of the volume fraction of the constituents. It is shown that the accuracy of any‐order expansion in perturbation method depends not only on the perturbation parameter, but also on the location chosen for the perturbation parameter and, in general, the solution becomes more accurate when the perturbation parameter is specified at the location where its corresponding response quantity is a maximum. Under thermal loading the possibility of using different parameters as the perturbation parameter for various boundary conditions is investigated. It is observed that, instead of a one‐parameter perturbation method, a two‐parameter perturbation method must be used in the thermal analysis of FG plates. Also, buckling and post‐buckling behavior of FG plates in cylindrical bending is investigated. It is shown that under thermal loading, a bifurcation‐type buckling occurs in clamped FG plates. In addition, a snap‐through buckling may occur in simply‐supported FG plates under thermo‐mechanical loading.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".