Stress distributions in nanocomposite sandwich cylinders reinforced by aggregated carbon nanotube
Bibliographic record
Abstract
In order to improve the static response of thick hollow cylinders, a sandwich cylinder with two carbon nanotube (CNT)‐reinforced nanocomposite face sheets are proposed in this article. Moreover, due to the use of optimum amount of high cost CNTs, the CNT distribution is suggested to be functionally graded (FG) along the thickness of cylinder. The stress and deflection profiles of the proposed sandwich cylinders subjected to internal and external pressures have been investigated using a finite element method (FEM) based on an axisymmetric model. The significant effect of formation of CNT agglomerations in the surrounded matrix is considered and the material properties of the resulted nanocomposite are estimated by Eshelby‐Mori‐Tanaka approach. Using the developed axisymmetric FEM model, the effects of CNT aggregation state, volume fraction, and distribution as well as geometrical dimension and loading condition on the stress and deflection distributions of the nanocomposite sandwich cylinders have been characterized. The extensive simulations have revealed that instead of adding higher volume fraction of CNT, the selection of suitable distribution for CNTs can lead to a nanocomposite sandwich cylinder with less deflection. POLYM. COMPOS., 40:E1918–E1927, 2019. © 2019 Society of Plastics Engineers
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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) | 0.000 | 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".