Graphene and CNT impact on heat transfer response of nanocomposite cylinders
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Reinforcing polymers with nanofillers is an advanced approach to improve and manage the thermal behaviors of polymeric nanocomposite materials. Among the proposed nanofillers, graphene and carbon nanotube (CNT) with superior thermal conductivity are two advanced nanofillers, which have extensively been utilized to enhance the heat transfer responses of host polymeric materials. In this work, the impacts of randomly oriented graphene and CNT to steady state and transient heat transfer behaviors of functionally graded (FG) nanocomposite cylinders have been investigated using an axisymmetric model. Nanocomposite cylinders have been assumed to be under heat fluxes, heat convections or temperatures as different types of thermal boundary conditions. The thermal properties of the resulted nanocomposite materials are estimated by micromechanical model. Moreover, the governing thermal equations of axisymmetric cylinders have been analyzed using a highly consistent and reliable developed mesh-free method. This numerical method predicts temperature fields via MLS shape functions and imposes essential boundary conditions with transformation approach. The effects of nanofiller content and distribution as well as thermal boundary conditions on the heat transfer responses of nanocomposite cylinders are studied. The results indicated that the use of nanofiller resulted in shorter stationary times and higher temperature gradients in FG nanocomposite cylinders. Moreover, the use of graphene in nanocomposites had stronger impact on thermal response than CNT.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it