Effects of Hall Current and Chemical Reaction on Magnetohydrodynamics Unsteady Heat and Mass Transfer of Casson Nanofluid Flow Through a Vertical Plate
Bibliographic record
Abstract
In this paper, we have reported the effects of Hall current on magnetohydrodynamics (MHD) unsteady heat and mass transfer of Casson nanofluid flow through a vertical plate in the presence of radiation and chemical reaction. The model equations have been used for the Casson nanofluid and they include the effects of thermophoresis and Brownian motion. Then, the obtained model equations have been transformed into nondimensional form by the usual mathematical procedure of transformation and the resultant nondimensional couple of partial differential equations are solved by explicit finite difference technique. Then, the obtained results are plotted after stability test by using the graphical software tecplot-9 and these results indicate the fluid flow, temperature, and concentration distributions which are significantly invaded by the variation of different dimensionless parameters such as magnetic parameter, Schmidt number, thermal Grashof number, Lewis number, Prandtl number, mass Grashof number, Dufour number, thermophoresis parameter, Brownian motion parameter, chemical reaction, and radiation parameter on velocity, temperature, and concentration along with the skin friction coefficient, Nusselt number, and Sherwood number. Further, the results have been discussed also with the help of graphs. Furthermore, it is observed that with the increase of the Casson parameter, velocity puts down, whereas by increasing the heat generation parameter, the temperature profiles are decreased.
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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 | 0.000 | 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".