Dynamic instability of elastically supported functionally graded porous arches reinforced with graphene platelets under a general dynamic load
Bibliographic record
Abstract
This paper investigates the in-plane dynamic instability behavior of elastically supported functionally graded porous (FGP) circular arches reinforced with graphene platelet-reinforced composite (FGP-GPLRC) under a general radial periodic dynamic load. By expressing the dynamic load in Fourier series, a comprehensive analysis of an FGP-GPLRC arch capable of dealing with various dynamic loading conditions is developed for the first time. The practical boundary conditions that are not fully rigidly restrained are modelled by elastic supports to enable a more accurate prediction of the dynamic stability of the arches. The governing equations of motion of the arch are derived and its dynamic instability regions are analytically determined. The present analysis is validated with excellent agreement with finite element results. A comprehensive parametric analysis is conducted to examine the effects of porosity distribution pattern, porosity coefficient, GPL mass fraction, elastic support, damping ratio, static load component, and dynamic load shape on the dynamic instability of the FGP-GPLRC arch. Our results indicate that the parametric resonance instability regions of an FGP-GPLRC arch under square-wave dynamic loads are significantly larger than those under sawtooth-wave and harmonic dynamic loads.
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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".