Sutterby hybrid nanofluid flow and heat transfer over a nonlinearly expanding sheet with convective boundary condition and zero-mass flux concentration
Bibliographic record
Abstract
This paper examines the impacts of a nonlinearly expanding sheet on velocity, heat and mass transport for a Suttery hybrid nanoliquid (mixture of Sutterby fluid and [Formula: see text] and [Formula: see text]. The zero-mass flux concentration and convective boundary conditions are considered. Nondimensionalization of governing equations may be achieved through similarity conversions. The governing equations are solved utilizing the Optimal Homotopy Analysis Method. Graphs and tables were used to document the effects of different variables. The numerical values for skin friction, Sherwood number and Nusselt number are provided in a table for diverse relevant factors. Comparisons were made to a previous study’s findings. The results obtained are in agreement with the findings of the prior study. The outcome is that the occurrence of hybrid nanoparticles ([Formula: see text] and [Formula: see text] in ethylene glycol liquid enhances its thermal conductivity thereby increasing the thermal boundary layer thickness. The presence of hybrid nanoparticles ([Formula: see text] and [Formula: see text] in ethylene glycol liquid also increases the momentum boundary layer thickness.
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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".