Dynamic response and optimization of functionally graded porous nanocomposite cylinders using a meshfree method
Bibliographic record
Abstract
Abstract A new class of advanced ultralight composite materials has recently emerged through the use porous polymer matrix reinforced by carbon nanotubes. In this article, the dynamic response of functionally graded porous polymeric cylinders, reinforced by randomly oriented single‐walled carbon nanotubes, using a meshfree method is studied. Three different porosity distribution patterns are investigated: symmetric distribution (SYD), unsymmetric distribution, and uniform distribution. A thorough study on the effects of reinforcement volume fractions and porosity distribution patterns on the dynamic response of the structure has been carried out using the radial point interpolation meshfree method based on the 2D theory of elasticity. In addition, a Pareto front solution is obtained through a multiobjective optimization aimed at minimizing the weight and maximizing the natural frequency of the structure with porosity and reinforcement volume fraction as design variables. From a design perspective, the results indicate that the SYD porosity type is the best candidate for relatively thick cylinders because of its smaller mass and higher stiffness compared to the other distribution types. The current research presents a reliable computational framework to help provide an insight into the design of an optimum structure subject to dynamic loading.
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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".