Vibration Response Analysis of Multilayer Functionally Graded Graphene Platelet-Reinforced Composite Cylindrical Shell Under Moving Random Loads
Bibliographic record
Abstract
This paper proposed a theoretical model for analyzing the vibration responses of multilayer functionally graded graphene platelet-reinforced composite (FG-GPLRC) cylindrical shell under moving random loads. Four GPLs distributed patterns and two ways of moving random load are taken into account. The proposed model is established by employing differential quadrature finite element method (DQFEM) combined with pseudo excitation method (PEM) and is solved by utilizing Newmark-[Formula: see text] method in the framework of first-order shear deformation shell theory (FSDST). The general boundary conditions of FG-GPLRC cylindrical shell structure are simulated by adopting the penalty function method. The effective material properties of multilayer FG-GPLRC cylindrical shell are derived based on the modified Halpin–Tsai model and mixture rule. Then, the convergence, accuracy, universality and robustness of the established model are verified by comparing the presented results with the corresponding results coming from finite element simulation software (ABAQUS and COMSOL) and the open literature. Finally, the influences of material parameters including the distribution pattern and weight fraction of GPLRC, structure parameters and the velocity and way of moving random load on the vibration response of multilayer FG-GPLRC cylindrical shell structure subjected to moving random loads are investigated systematically. This research can provide the theoretical basis for evaluating the vibration response of multilayer FG-GPLRC cylindrical shell structure subjected to moving random loads.
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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) | 0.001 | 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".