A multiscale modeling technique for buckling analysis of rectangular multiphase nanocomposite plates reinforced with alumina nanoparticles and discontinuous carbon fibers
Bibliographic record
Abstract
This study presents the buckling analysis of rectangular multiphase nanocomposite plates reinforced with alumina nanoparticles and discontinuous carbon fibers (DCFs), resting on an elastic foundation and subjected to different boundary conditions. A key contribution of this work is developing a multiscale computational framework that bridges microscale material modeling and macroscale structural analysis. The mechanical properties of nano-alumina/DCF/polymer nanocomposites are estimated using a micromechanical model considering important microstructures. The multiphase nanocomposite plate is modeled using the first-order shear deformation theory (FSDT), while the Winkler and Pasternak foundation models are employed to simulate the substrate. By constructing the system’s total potential energy functional and applying the p-Ritz method, numerical results are generated to investigate the influences of percentage, diameter and agglomeration of nano-alumina, size and stiffness of the nanoparticle/polymer interfacial layer, volume fraction and aspect ratio of DCFs, elastic foundation characteristics, and geometric parameters of the plate on the critical buckling loads. Three buckling cases namely uniaxial, biaxial, and shear buckling, are analyzed. It is observed that dispersing the alumina nanoparticles into the polymer matrix of DCFs increases the structural rigidity and elevates the critical buckling load. Larger nanoparticle diameters lead to a decline in buckling resistance of the multiphase nanocomposite plates.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".