Unsteady heat and mass transfer magnetohydrodynamic (MHD) nanofluid flow over a stretching sheet with heat source–sink using quasi-linearization technique
Bibliographic record
Abstract
The current study deals with two-dimensional unsteady incompressible MHD water-based nanofluid flow over a convectively heated stretching sheet by considering Buongiorno’s model. A uniform magnetic field is applied in the direction normal to the stretching sheet. It is assumed that the lower surface of the sheet is heated by convection by a nanofluid at temperature Tf, which generates the heat transfer coefficient, hf. Uniform temperature and nanofluid volume fraction are assumed at the sheet’s surface and the flux of the nanoparticle is taken to be zero. The assumption of zero nanoparticle flux at the sheet’s surface makes the model physically more realistic. The effects of the uniform heat source–sink are included in the energy equation. With the help of similarity transformations, the partial differential equations of momentum, energy, and nanoparticle concentration are reduced to a system of nonlinear ordinary differential equations along with the transformed boundary conditions. The derived equations are solved with the help of the quasi-qinearization technique. The model is solved by considering the realistic values for the Lewis number, thermophoresis, and Brownian motion parameters. The objective of the current study is (i) to provide an efficient numerical technique for solving the boundary layer flow model and (ii) introduction of zero nanoparticle flux on the convectively heated stretching surface. The current study also focuses on the physical relevance and accurate trends of the boundary layer profiles, which are adequate in the laminar boundary layer theory. The dependence of the nanoparticle volume fraction and other pertinent parameters on the dimensionless velocity, temperature, shear stress, and heat transfer rates over the stretching surface are presented in the form of profiles.
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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".