Nonlinear thermal static and dynamic buckling of imperfect porous functionally graded cylindrical shells reinforced with oblique stiffeners embedded within nonuniform elastic medium
Bibliographic record
Abstract
This study takes an analytical technique to examine the thermal post-buckling behaviors of imperfect oblique stiffened porous functionally graded cylindrical shells under a thermal environment. The oblique stiffened porous functionally graded cylindrical shells are embedded within a nonuniform elastic medium, whose parameters utilized in the study are determined via Selvadurai’s methodology. In this work, two types of porous functionally graded materials with uniformly and nonuniformly distributed porosities are used for the internal stiffeners and shell. The stiffeners are modeled utilizing Lekhnitskii’s smeared stiffeners technique, and stiffened porous functionally graded cylindrical shells with oblique stiffeners at various angles are investigated. The nonlinear governing equations are formulated via the Donnell shell theory and von-Kármán equation. Then the equation is discretized utilizing Galerkin’s approach to facilitate analysis of the shells’ behavior. The P-T method is employed to determine dynamic thermal post-buckling responses. Therefore, the study looks into material properties, nonuniform elastic medium parameters, and the impact of stiffeners at different angles. The findings of the current research provide valuable insights for researchers and engineers engaged in analyzing and designing oblique stiffened porous functionally graded cylindrical shells integrated with nonuniform elastic medium.
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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) | 0.000 | 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".