Analysis of heat transportation in a convectively heated time-dependent <i> CuAl <sub>2</sub> O <sub>3</sub> -H <sub>2</sub> O </i> hybrid nanofluid with varying thermal conductivity
Bibliographic record
Abstract
This study presents a numerical investigation of the hybrid nanofluid [Formula: see text] and heat transportation over a convectively unsteady heated stretching sheet. Thermal conductivity is considered temperature dependent function. The set of non-dimensional differential equations is converted into nonlinear single independent variable equations by adopting the suitable transformations. The numeric results for the present set of transformed equations are executed through the RKF (Runge-Kutta-Fehlberg) process that is based on the shooting approach. The impact of several affecting parameters on temperature, local Nusselt number [Formula: see text] skin-friction factor [Formula: see text] and velocity are illustrated through graphs. A comparison has been done with the previously published material and decent agreement is achieved. The boundary-layer thickness and temperature profile are improved for both nanoparticles [Formula: see text] and [Formula: see text] added to the base fluid. However, the velocity profile illustrates an opposite attitude. The positive values of Biot number closer to the surface are obtained by increasing heat transportation rate.
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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".