A comparative study on magnetic and non-magnetic particles in nanofluid propagating over a wedge
Bibliographic record
Abstract
The purpose of this paper is to investigate convective heat and mass transfer of nanofluid in the context of improving physical properties through magnetic and non-magnetic nanomaterials under the magnetic influence. For this, three magnetic nanoparticles: cast iron, pure iron, and magnetite and three non-magnetic: gold, silver, and copper are taken into account. The physical problem for homogenous nanofluid is modeled by employing the magnetic interaction between nanoparticles through Lorentz force into fundamental equations of thermo-hydrodynamic and correlations models that support effective physical properties. The governing equations in dimensionless form are taken to analyse the nanofluid flow as well as heat profile. The impact of interesting physical parameters like particle volume fraction and magnetic field on patterns of velocity and the temperature are graphically demonstrated and discussed. The effect of concentration and size of nanoparticles on shear stress and heat transfer at the wall are examined through numerical values shown in table form. The results show that the heat transfer rate of a base fluid is enhanced by deploying nanoparticles and further improved by taking small-size magnetic nanoparticles.
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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".