Frequency Assessment of Sandwich Rectangular Plates with an Anisogrid Core and GPLRC Face Sheets
Bibliographic record
Abstract
In this paper, the free vibration of a sandwich plate with an anisogrid core and two face sheets reinforced with graphene platelets (GPLs) is investigated. A continuous approach is considered for the lattice core and the equivalent properties are calculated. Adopting the Halpin–Tsai micromechanics, the effective Young’s modulus of the nanocomposites/graphene platelets is extracted. Also, mass density and Poisson’s ratio are earned with the simple rule of mixtures. A quasi-3D theory is applied to model the kinematics of the sandwich plate with simply supported boundary conditions. Hamilton’s principle is implemented to obtain the equations of motion that are solved based on the Navier solution. The validity of the results of this study is confirmed by comparing the analytical results with those presented in other researches and also a finite element model. The effect of the parameters of the lattice core such as the width of ribs, the number of helical ribs in one direction, and the ratio of thickness of face sheets to core on the natural frequencies of the sandwich plate was investigated. Additionally, the impact of the pattern of graphene platelets and their weight fraction on the natural frequencies were investigated. The results show that by decreasing the ratio of the thickness of face sheets to the thickness of core and increasing the number of ribs and their width, the natural frequencies will decrease. Moreover, the patterns FG-V and FG-A have the highest and the lowest natural frequencies, respectively, among the other distribution of graphene platelets.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".