Analysis of vibration characteristics and buckling behaviour of rotating fiber–graphene-reinforced composite pre-twisted shells
Bibliographic record
Abstract
This study presents a novel approach to analyze the vibration and buckling behaviour of pre-twisted fiber-reinforced polymer composite shells reinforced with graphene inclusions. A key novelty lies in incorporating graphene’s size-dependent mechanical properties are derived from nanoscopic empirical equations into the analysis. This allows for a more accurate prediction of the overall mechanical response of the composite, particularly at the nanoscale. The Halpin–Tsai model is employed to determine the equivalent elastic constants of the graphene-reinforced matrix, and a finite element formulation based on curved shear deformable shell theory is developed. The model’s accuracy is validated against existing experimental or numerical results. Also, this study provides a comprehensive parametric analysis, investigating the influence of key factors, such as graphene volume fraction, twist angle, aspect ratio, hub radius, and rotation speed on the vibration frequency and buckling load of the pre-twisted shells. These findings offer valuable insights for the design and optimization of lightweight and high-performance composite structures utilized in aerospace, automotive, and other engineering applications.
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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) | 0.000 | 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".