A computational analysis of the double-diffusive effect on heat absorbing and radiative MHD flow of nanofluid across a mobile vertical porous membrane
Bibliographic record
Abstract
This study presents a comprehensive analysis of the double diffusion effects (Soret and Dufour) on magnetohydrodynamic (MHD) heat-generating nanofluid flow of water-based fluids containing Cu and TiO2 nanoparticles past a moving, inclined, and absorbent plate under the influence of prescribed heat flux. The mathematical model is formulated by transforming the governing nonlinear partial differential equations into a dimensionless form using suitable nondimensional parameters. The resulting system of quasilinear PDEs, subject to appropriate initial and boundary conditions, is solved numerically using an implicit Crank-Nicolson finite difference method coded in MATLAB. The study examines the impact of key physical parameters, including magnetic field strength, heat source/sink, radiation-absorption coefficient, radiative heat flux, Prandtl number, Dufour number, Soret number, and plate inclination on velocity, temperature, and concentration distributions, presented through graphical illustrations. It is observed that increasing the Soret number enhances fluid velocity and solutal concentration, while an increase in magnetic field strength, heat flux, and plate inclination angle suppresses the flow velocity. Furthermore, the rate of heat transfer improves with rising radiation-absorption, heat source, and radiative heat flux parameters, whereas it diminishes with higher Prandtl numbers. The wall shear stress, Nusselt number, and Sherwood number are computed and tabulated to highlight changes in boundary-layer transport characteristics. Comparatively, Cu nanoparticles demonstrate higher thermal performance than TiO2, highlighting their effectiveness in enhancing energy transport. Validation against previously published results confirms the reliability of the numerical method and the physical soundness of the model.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".