Melting heat transfer in Eyring–Prandtl fluid flow past a nonlinear stretching sheet in presence of slip
Bibliographic record
Abstract
The study examines two-dimensional Eyring–Prandtl fluid flow and melting heat transfer past a nonlinear stretching sheet in presence of boundary slip. To get the self-similar structure of the leading equations, similarity transformations are employed. Similar solutions to the system have been identified for a prescribed power law velocity of a stretching sheet. The main goal of this work is to explore the melting heat transfer in boundary layer slip flow of Eyring–Prandtl fluid flowing over a nonlinear stretching sheet. Inclusion of boundary slip and melting heat transfer makes the problem different from other available research works, which indicates the novelty of the present work. After solving the self-similar nonlinear equations numerically, the data are presented through graphs and tables to identify the nature of velocity, velocity gradient, and temperature for various parametric values. Detailed descriptions of the effects of different parameters on velocity, velocity gradient, and temperature along with physical justifications have been provided as comprehensively as possible. Fluid velocity is seen to decrease when the slip parameter and fluid material parameter β expand, whereas the temperature is seen to rise under such circumstances. Velocity and temperature are diminished by the increasing melting parameter m resulting a thinner thermal and momentum boundary layers. It is evident that the melting parameter causes to reduce the thermal boundary layer while due to the boundary slip, the thermal boundary layer enhances.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 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".