Squeezing flow in the existence of carbon nanotubes past a Riga plate
Bibliographic record
Abstract
Squeezing or squeeze flows have tremendous applications in applied fields, like engineering, biomedical sciences and rheological studies. This paper demonstrates the squeezing flow of kerosene-based nanoliquids between parallelly aligned plates, with a Riga-type fixed lower boundary. The effects of dispersing two types of copper-functionalized carbon nanotubes (CNTs), single-walled CNTs (SWCNTs) and multi-walled CNTs (MWCNTs) are examined. Using appropriate transformations, a self-similar ordinary differential system is derived from the governing model of partial differential equations and substantial boundary conditions. Using the homotopy analysis method (HAM) and the [Formula: see text] package, analytical and numerical estimates are obtained, respectively. For higher values of the squeezing parameter, dimensionless temperature increases, while velocity patterns are upside-down. Moreover, increments in dimensionless parameters representing Riga constituent width, modified Hartmann number, nanoparticle concentration and radiation parameter improve heat transfer rates and thermal boundary layer thickness, however, adversely affect velocity. Despite enhanced friction effects, numerical results show that the Nusselt number increases as nanoparticle loads and radiation parameters increase. This suggests that convection rates are improved over conduction rates. Excellent agreement is found between analytical and numerical evaluations. Apparently, it is noticed that MWCNTs perform better than SWCNTs.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".