Thermoelastic static and vibrational behaviors of nanocomposite thick cylinders reinforced with graphene
Bibliographic record
Abstract
Current paper deals with thermoelastic static and free vibrational behaviors of axisymmetric thick cylinders reinforced with functionally graded (FG) randomly oriented graphene subjected to internal pressure and thermal gradient loads. The heat transfer and mechanical analyses of randomly oriented graphene reinforced nanocomposite (GRNC) cylinders are facilitated by developing a weak form mesh free method based on moving least squares (MLS) shape functions. Furthermore, in order to estimate the material properties of GRNC with temperature dependent components, a modified Halpin Tsai model incorporated with two efficiency parameters is utilized. It is assumed that the distributions of graphene nano sheets are uniform and FG along the radial direction of nanocomposite cylinders. By comparing with the exact result, the accuracy of the developed method is verified. Also, the convergence of the method is successfully confirmed. Then we investigated the effects of graphene distribution and volume fraction as well as thermo mechanical boundary conditions on the temperature distribution, static response and natural frequency of the considered FG GRNC thick cylinders. The results disclosed that graphene distribution has significant effects on the temperature and hoop stress distributions of FG GRNC cylinders. However, the volume fraction of graphene has stronger effect on the natural frequencies of the considered thick cylinders than its distribution.
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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".