Multiscale analysis of elastodynamics of graphene-embedded ceramic composite plates
Bibliographic record
Abstract
The performance of graphene–silicon carbide (SiC) composite multilayered structure under various transverse impact loading conditions is considered in this paper. This prototypical system is examined using a multiscale approach which integrates ReaxFF molecular dynamics with Reddy’s third-order shear deformation plate theory in a hierarchical framework. In essence, the developed multiscale analysis combines the simulation of material properties (i.e., graphene nanofiller and the ceramic matrix) at the atomic scale and the mechanics of the structure at the macroscale. Accordingly, the governing equations of the aforementioned system are discretized and solved by utilizing a meshfree method. In that regard, the elastodynamics of such composites is characterized by factoring in constituent materials properties and nanofiller volume fraction. Comprehensive numerical simulations, corroborated by some of the available experimental evidence from the existing reports, reveal that (a) oxidation degree of the graphene nanofiller can be introduced as a novel tuning factor for the elastodynamic response of the macroscale graphene–ceramic composite structures, and (b) higher volume fraction of graphene enhances the flexibility and induces larger deflection of the composite plate under various dynamic loadings (softening effect). Furthermore, the dependency of the results on the structural boundary conditions is assessed. The multiscale approach and findings of this study offer insights into the feasible bottom-up design pathways for developing novel multilayered ceramic matrix composites with graphene inclusion for applications in structural engineering, energy devices, and aerospace industries.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".