Numerical analysis of Ag–CuO/water rotating hybrid nanofluid with heat generation and absorption
Bibliographic record
Abstract
The present work is committed to examining the impacts of magnetohydrodynamics (MHD), heat generation–absorption, and volume fraction of nanoparticles on the flow of hybrid nanofluid past a stretching surface. The comparison of heat transfer properties of rotating, conventional nanofluid with that of developing hybrid nanofluid is also studied. To examine the Lorentz force impacts on three-dimensional stretching surface, another model of “thermophysical properties” is used. The whole system, including nanofluid and stretching surface, is in rigid body rotation about an axis normal to the plane of the stretching surface with constant angular velocity. The system of governing nonlinear partial differential equations has been simplified by using suitable similarity transformations and then solved via an efficient numerical technique, BVP-4C. The velocity and local skin friction are obtained in both directions. The rate of heat transfer is determined on the surface. The effects of pertinent physical parameters, which are magnetic parameter, rotation parameter, stretching parameter, heat generation or absorption parameter, and Prandtl number, have been discussed through graphical and tabular form. From the present study, it is noticed that the rate of heat transfer of hybrid nanofluid is higher than that of ordinary nanofluid. In hybrid nanofluid, the required rate of heat transfer can be accomplished by picking distinctive and suitable nanoparticle extents.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".