Entropy generation in hydromagnetic nanofluids flow inside a tilted square enclosure under local thermal nonequilibrium condition
Bibliographic record
Abstract
This paper investigates the entropy generations due to the multiple factors such as base fluid, nanoparticles, viscosity, and the inclined magnetic field for the convective flow of nanofluids inside a titled square enclosure considering local thermal nonequilibrium (LTNE) condition. One of the walls of the enclosure is kept heated changing its position such as (a) bottom, (b) near, (c) top, and (d) vertical while the corresponding opposite wall is kept cold. The other two walls are reserved insulated. The dimensionless constituting equations of the physical model are simulated numerically using FEM (finite element method). The numerically simulated data are matched with the data available in the open literature and noticed excellent agreement among them. The results indicate that nanoparticle loading and the Nield number strongly control the LTNE conditions among the regular fluid and nanoparticles. The Rayleigh and Hartmann numbers intensely control the system irreversibility while the magnetic field leaning angle acts slightly on it. We identified that fluid friction mainly contributes to the total entropy generation. The system produced maximum entropy for a bottom heated wall whereas the minimum entropy for the top heated wall. The entropy generated due to heat transfer dominates the frictional entropy only for the top heated wall.
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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) | 0.000 | 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".