Bending response analysis of a laminated, tapered, curved, composite panel made from an agglomerated and wavy MWCNT–glass fiber–polymer hybrid
Bibliographic record
Abstract
The work investigates the influence of multiwalled carbon nanotubes (MWCNTs) on the bending behavior of laminated, spherical, cylindrical, hyperbolic, and elliptical tapered composite panels made from a MWCNT–glass fiber–polymer hybrid and subjected to transverse loading conditions. The deflection and stress behavior of the composite panels were studied by developing a mathematical model based on high-order shear deformation theory using finite element (FE) formulation. In this context, the agglomeration and waviness of MWCNTs were modeled and characterized using the Eshelby–Mori–Tanaka approach and a continuum mechanics based 3-D representative volume element (RVE), respectively. Subsequently, glass fiber was introduced as a reinforcement phase, and the elastic properties of the three-phase hybrid composite material were obtained using the Chamis model. The developed FE formulation was validated theoretically and experimentally. Further, detailed parametric studies were performed to examine the influence of micromechanical and structural characteristics such as weight fraction of MWCNTs, weight fraction of fiber, type of load, taper configuration, curved geometry, curvature ratio, and length to thickness ratio of the panel on the bending behavior of the composite panels. The effective laminated tapered curved composite panel, TC-3, tailored with improved MWCNT characteristics, can substantially resist the stresses from a bending load.
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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".