Cross-Sectional Design and Analysis of Multiscale Carbon Nanotubes-Reinforced Composite Beams and Blades
Bibliographic record
Abstract
The present work addresses with the cross-sectional design and analysis of fiber-reinforced multiscale composite beams of general cross-sectional shape and arbitrary anisotropic material properties and investigates the effect of carbon nanotubes (CNTs) on their stiffness properties. The three-dimensional strain field was formulated in terms of one-dimensional strains and a three-dimensional warping displacement. The bulk material properties of the multiscale composite were predicted using Halpin–Tsai equations and fiber micromechanics. The carbon nanotubes were assumed to be uniformly distributed and randomly oriented throughout the polymer matrix. The variational asymptotic beam section (VABS) was used to numerically evaluate the stiffness and mass matrices of four test cases: strip, circular pipe, box beam and airfoil. The influence of CNTs weight percentage and volume fraction of fibers was investigated through a detailed parametric study. The numerical results indicate that the inclusion of a small weight percentage of carbon nanotubes in the polymer matrix is sufficient to induce a significant improvement in stiffness properties.
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".