Lie group analysis and numerical solutions for magneto-convective slip flow of nanofluid over a moving plate with Newtonian heating boundary condition
Bibliographic record
Abstract
Magnetohydrodynamic laminar boundary layer slip flow of a nanofluid over a moving plate with Newtonian heating boundary condition in the presence of heat generation–absorption effects is studied using Lie group analysis and a numerical method. The model used for the nanofluid includes the effects of Brownian motion and thermophoresis. The governing transport equations are non-dimensionalized and transformed into a set of similarity equations using similarity transformations generated by Lie group transformations. The transformed equations are then solved using the Runge–Kutta–Fehlberg fourth- and fifth-order numerical method in Maple 17, which is also used to generate relevant graphs and tables. The flow, heat, and nanoparticle volume fraction characteristics are shown to depend on a number of thermophysical parameters, namely, Brownian motion, thermophoresis, Lewis number, Prandtl number, linear momentum slip, magnetic field, suction–injection, Newtonian heating, and heat generation–absorption. The effects of these parameters on the dimensionless stream function, velocity, temperature, nanoparticle volume fraction, wall heat, and mass transfer rates are investigated. Comparisons of the present numerical solutions with published works show very good correlation. The study finds applications in nano-technological magnetic materials processing.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".