Analysis of convective heat transfer in triple diffusive free convection flow of Williamson nanofluid along a horizontal plate
Bibliographic record
Abstract
This research investigates the concurrent effects of three different diffusion mechanisms, species diffusion, thermal diffusion, and nanoparticle diffusion, on the convective heat transfer and mass transfer properties of a nanofluid. As a part of the study, a further level of complexity is added using the convective boundary condition by considering the interaction between surface heat flow and surrounding fluid dynamics. Further the transformed governing equations are obtained with the help of similarity solutions. The Runge-Kutta-Fehlberg method is used with the shooting approach to get numerical solutions for the simplified equations. The influences of various involved parameters on velocity profiles, temperature profiles, concentration, local skin friction, local Nusselt and Sherwood numbers are discussed. The significant outcomes of the study are observed as the decrease in the velocity and fluid temperature during the increase of the suction parameters, and the indication of the evacuation of fluid from the surface that lessens the heat transmission and lowers the temperature profiles. The fluid temperature is raised by the augmentation in the conductive parameter. It has also been found that the mass transfer rate increased as the salts are included. The heat transfer decreases with higher thermophoresis strength but increases with higher Prandtl number.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".