A non-Fourier double diffusion theory-based optimal assessment for 3D flow of Prandtl nanofluid featuring Hall and ion slip impacts through convectively heated surface
Bibliographic record
Abstract
Thermodynamic properties of non-Newtonian fluids through stretching surfaces become widely used in processes such as the extrusion of plastic sheets, paper manufacturing, glass blowing, liquid film condensation, and biopolymer cylinder coatings. Consequently, the aim of this communication is to transparently describe the Prandtl nanofluid flow characteristic that results from extending a surface. In the present endeavor, heat & mass transfer are combined by means of the generalized Ohm law. The generalized Fourier and Fick laws serve as the foundation for the phenomena of mass movement and heat transfer, respectively. Ion slip effects and the Hall contribution are also included. The two key slip mechanisms that control nanoparticle mobility in relation to the base fluid—Brownian diffusion and thermophoresis—are taken into consideration in this work by using Buongiorno's nanofluid model. Brownian motion and thermophoresis parameters are used to quantify these phenomena, respectively. Based on their capacity to regulate velocity using thermophoresis and Brownian motion parameters, the results showed that nanoparticles are useful for both medication delivery and transportation. These sets of partial differential equation (PDEs) that correspond to the mathematical modeling are coupled with appropriate similarity transformations to build an (ordinary differential equations) system, which is subsequently solved by consuming the Lobatto IIIA method's power. Numerical and graphical examples are provided to illustrate the ways in which different physical restrictions impact heat and mass transport in addition to velocity fluctuation. According to the results, the velocity profile tends to be improved by the Prandtl and elastic fluid parameters. Moreover, Random movement of fluid particles, which is brought on by an increase in the Brownian motion parameter, might intensify the heat transfer phenomenon. Many real-world domains, such as microfluidics, industry, transportation, the military, and medical, use features of nanofluids. The present research is significant and might also be helpful in raising heat exchangers' thermal efficiency, which would support the preservation of thermal balance control in heat-density devices and equipment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".