Torsional Buckling and Post-Buckling Behavior of Sandwich FG Toroidal Shell Segments Featuring FG Porous Core
Bibliographic record
Abstract
This research addresses the torsional buckling and post-buckling of sandwich functionally graded (FG) toroidal shell segments (SFG-TSSs) featuring FG porous (FGP) core utilizing the semi-analytical approach. The shells consist of a tri-layered configuration, where the top and bottom layers are FG, and the core consists of FGP material. Also, three types of SFG-TSSs, including concave toroidal shell (Con-TS), convex toroidal shell (Cov-TS), and cylindrical shell (CS), are investigated, all of which are exposed to torsional excitation. In addition, two types of FGP cores are considered in this study: one with uniform porosity distribution (UPD) and one with non-UPD (NUPD). The nonlinear governing equations (NGEs) are derived based on Donnell shell theory (DST), incorporating von Kármán-type geometric nonlinearities. Galerkin’s method is employed to discretize the NGEs, and an approximate three-term solution for the deflection is formulated. Therefore, analytical expressions are formulated to evaluate the critical torsional buckling load (CTBL) and to illustrate the torsional post-buckling load–deflection behaviors. The results obtained in this study are verified through comparisons with relevant findings reported in the existing literature. Importantly, this research emphasizes the impact of various parameters on improving the stability of SFG-TSSs. The outcomes presented here can serve as valuable references for researchers and engineers engaged in the design and analysis of SFG-TSSs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".