Small-scale effects on transverse vibrational behavior of single-walled carbon nanotubes with arbitrary boundary conditions
Bibliographic record
Abstract
In the present paper, the differential transformation method is employed to develop a semianalytical solution for free transverse vibration of single-walled carbon nanotube (SWCNT) with arbitrary boundary conditions. The small scale effect is taken into consideration via Eringen's nonlocal elasticity theory while the transverse shear deformation effects and rotary inertia are taken into account in presented Timoshenko beam theory. Through variational formulation and the Hamilton's principle the governing differential equations and the boundary conditions are derived and then solved by a semi-analytical method called differential transformation method (DTM) for various frequency modes of beams and different edge conditions. Comparisons made between the present results and results reported by well-known references for special cases treated before, have confirmed accuracy and efficiency of the presented approach. The effects of several parameters such as transverse shear deformation effects, slenderness ratios, boundary conditions and small scale on vibration characteristics of SWCNT are examined. The present study illustrates that the vibration characteristics of an SWCNT are strongly dependent on the small scale parameters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".