Magneto Hydrodynamic Effect on Nanofluid Flow and Heat Transfer in Backward- Facing Step Using Two-Phase Model
Bibliographic record
Abstract
Magneto hydrodynamics effects on nanofluid flow in backward-facing step is studied using two-fluid model of Buongiorno. Due to the utilization of two-phase model, variable nanoparticle concentration and nanofluid properties are considered. Thermophoresis and Brownian diffusivities are calculated in particle dispersion. Effects of Reynolds number, particle volume fraction, magnetic field and Hartmann numbers are studied on heat transfer and fluid flow characteristics. It is shown that introduction of nanoparticles as a second phase, pushes reattachment point further into the downstream, while magnetic field has opposite effect and pushes it backward into the upstream. Particles are shown to be migrating from hot to cold regions due to the dispersion mechanisms considered. In comparison to single phase models, there is 3.7% decrease in maximum Nusselt number and more than 40% difference in the reattachment point location. Accuracy of the reattachment point is shown through previous pure fluid studies, the comparison to which show less than 0.8% tolerance with most recent studies. Relative effect of diffusion mechanisms is compared in different flow conditions, which show up to 12.5% difference. Application of magnetic field results in average Nusselt number increase of more than 10% by Hartmann number of 12.
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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 | 0.001 | 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".