Insight into the convective heat transport in magnetohydrodynamic Casson liquid inside a wavy channel
Bibliographic record
Abstract
This study explores the magnetohydrodynamic (MHD) behavior of a non-Newtonian Casson fluid flowing through a wavy channel featuring a localized heat source at the bottom wall, with emphasis on the interplay between magnetic field effects and thermal transport characteristics. Vorticity-stream function method is employed to derive the vorticity transport equation along with stream function equation. The resulting coupled, nonlinear MHD and the energy equation together with physically relevant boundary conditions are solved numerically using finite difference technique. The key flow parameters, viz., Reynolds number (Re), Rayleigh number (Ra), Casson parameter (β), Hartmann number (Ha), and Prandtl number (Pr), have noteworthy impacts on the motion of the fluid and on the thermal distribution. Results indicate that with an amplification in the Casson parameter (β), fluid velocity is amplified in magnitude and a lessening in the breadth of the thermal boundary layer close to the heater is noted. Suppressive behaviour of the Lorentz force on convection causes the decline of rate of heat transport with rising Hartmann number (Ha). Additionally, a reduction in backflow region is experienced due to ascending Prandtl number (Pr) and Reynolds number (Re). Further, it is established that average Nusselt number [Formula: see text] is a growing function of Re and Pr, demonstrating improved heat transport characteristics under these conditions.
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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 | 0.001 |
| 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".