Buckling analysis of bio-inspired helicoidal functionally graded CNT-reinforced laminated nanoplates with antisymmetric angle-ply architecture
Bibliographic record
Abstract
:This study presents a novel Galerkin-based analytical framework for the comprehensive investigation of the buckling behavior of advanced functionally graded (FG), antisymmetric angle-ply (AP), bio-inspired helicoidal (BiH) laminated composite nanoplates. To the best of our knowledge, this is the first analytical exploration of such hybrid nanoscale structures, integrating both material and geometrical gradation effects. The proposed model accounts for nanoscale reinforcement via randomly dispersed single-walled carbon nanotubes (SWCNTs) in conjunction with FG fibrous reinforcements, thereby enhancing both mechanical performance and multifunctionality. The governing stability equations are rigorously derived through the principle of virtual work, incorporating higher-order shear deformation theory (HSDT) and an advanced nonlocal strain gradient elasticity theory to capture essential small-scale effects and microstructural interactions. Three distinct helicoidal carbon nanotube (CNT) arrangements are considered: helicoidal-linear (HL), helicoidal-exponential (HE), and helicoidal-semicircular (HS), alongside four CNT distribution profiles: uniform distribution (UD), functionally graded X-type (FG-X), functionally graded O-type (FG-O), and functionally graded asymmetric (FG-A). The combined influence of these reinforcement schemes and helicoidal architectures is systematically examined to characterize their impact on the global buckling resistance of the nanoplates. A detailed parametric investigation is conducted to explore the effects of volume fraction, layer thickness ratio, gradient index, aspect ratio, and various boundary condition types. The findings provide deep insights into the synergistic interplay between reinforcement topology, material gradation, and scale-dependent behaviors, thus offering valuable guidelines for the optimal design and deployment of next-generation nanostructured composite systems in aerospace, biomedical, and structural 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".