Determination of thermoelastic stress wave propagation in nanocomposite sandwich plates reinforced by clusters of carbon nanotubes
Bibliographic record
Abstract
Adding small amounts of carbon nanotubes (CNTs) into the face sheets of sandwich structures can significantly improve their thermo-mechanical responses. However, the formation of CNT clusters, especially at high volume fractions of CNTs, dramatically affects the mechanical properties of the resulted nanocomposites, which is usually ignored. In this paper, by considering the formation of CNT clusters, we have investigated transient heat transfer and stress wave propagation in polymeric sandwich plates with two nanocomposite face sheets. The face sheets were made of clusters of CNTs embedded in a polymeric matrix. The volume fractions of CNTs and their clusters were assumed to be functionally graded along the thickness of face sheets. The proposed sandwich plate was subjected to thermal and impact pressure loads. Eshelby–Mori–Tanaka’s approach was applied to evaluate the material properties of the resulted nanocomposite with components with temperature-dependent material properties. Reddy’s third-order shear deformation theory and a moving least square shape function-based mesh-free method were utilized for thermoelastic dynamic analysis. The effects of CNT cluster size, distribution, and volume fraction as well as thermal load on the thermoelastic dynamic behavior of nanocomposite sandwich plates were investigated. It was observed that the distribution and cluster size of CNTs had significant effects on the amplitude and speed of thermoelastic stress wave propagation in the nanocomposite sandwich plates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".