Thermal dynamics and magnetohydrodynamics in ferrofluidic wall jet flow: Entropy generation in heat and mass transfer
Bibliographic record
Abstract
Wall jet nanofluids with entropy generation possess several applications in cooling electronic devices, and solar collectors. The unique magnetic properties of the ferro-nanoparticles allow for the precise control of fluid flow using external magnetic fields, which is invaluable for targeted cooling or heating. In this study, we investigate wall jet hybrid nanofluid materials with ferrous-ferric oxide and copper oxide in conventional fluid water. The governing velocity, mass, and heat transfer equations are calculated to a set of ordinary differential equations (ODEs) via similarity parameters that are solved numerically. Effects of physical parameters, including thermophoretic parameters, Brownian motion parameters, and magnetic parameters, on velocities, temperature and entropy generations are analyzed using graphical representations. The results show that rising the Brownian motion, the magnetic term, or the thermophoresis term rises the fluid temperature. Furthermore, Brownian motion, or the thermophoresis effect increases temperature more for the ferro-hybrid nanofluids than that for single nanofluid. Increasing the thermophoretic parameters and Brown motion lead to the decay of the entropy generation due to enhanced thermal gradients and particle movement. However, the entropy generation enhances as the thermal radiation term rises. This demonstrates that the hybrid nanofluids can raise the thermal and mass transfer but no effects on velocities and entropy generation, compared to the single nanofluid.
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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".