Nonlinear torsional vibration and dynamic post-buckling responses of spiral stiffened functionally graded porous cylindrical shells
Bibliographic record
Abstract
This study employs a semi-analytical approach to investigate the nonlinear torsional vibration and dynamic torsional post-buckling (DTPB) responses of spiral stiffened functionally graded (FG) porous (SSFGP) cylindrical shells. These shells are resting on a generalized nonlinear viscoelastic foundation (GNVEF). This foundation consists of a dual-parameter Winkler-Pasternak foundation augmented by a Kelvin-Voigt viscoelastic model. The model includes nonlinear cubic stiffness and takes damping effects into consideration. Within the scope of this research, two variations of SSFGP cylindrical shells are examined: those characterized by non-uniform and uniform porosity distributions. Employing the Donnell shell theory, von-Kármán nonlinear geometric assumptions, and Galerkin’s method, a discretized nonlinear governing equation is derived to analyze the behaviors of the shells. Consequently, explicit formulations for dynamic torsional load are meticulously obtained. The findings of the present study are validated by comparing them with the outcomes documented in existing literature, as well as through alignment with the P-T method. This research delves into the system’s nonlinear behaviors, with scrutinization of the effects of diverse factors such as material and geometrical parameters. The researchers and engineers in this field may use the findings of this research as benchmarks for their design and research of SSFGP cylindrical shells.
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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".