Damped harmonic vibrations of axisymmetric graphene‐enhanced cylinders in thermal environment
Bibliographic record
Abstract
Abstract In this paper, the structural damping behavior of axisymmetric nanocomposite cylinders enhanced with randomly oriented graphene nanosheets has been explored by a mesh‐free solution. The static responses of such nanocomposite cylinders under internal pressure and exposed to thermal environment are assumed as the initial condition of harmonic vibrations. Using the shape functions of moving least squares (MLS), an axisymmetric mesh‐free solution has been developed to approximate the displacement field of the graphene‐enhanced nanocomposite (GEN) cylinders. Along the thickness of these cylinders, different nonlinear functionally graded (FG) patterns are considered for the distribution of graphene nanosheets. The mechanical properties of graphene and polymer are considered to vary with temperature, and the overall properties of nanocomposite are calculated using a modified Halpin‐Tsai (HS) technique. The effects of thermal environment, graphene content, graphene dispersion, and cylinder dimension on the structural damped harmonic vibrations of axisymmetric GEN cylinders have been examined. The results indicate that the use of graphene nanosheets and their distribution significantly affect the damped harmonic vibrations of axisymmetric polymeric cylinders such that the increase of graphene content results in damped vibrations with higher frequency and shorter stationary time. In addition, thermal environment reduced the frequency of vibrations, but it has an insignificant impact on the stationary time of vibrations.
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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".