MHD Nanoliquid Flow Along a Stretched Surface with Thermal Radiation and Chemical Reaction Effects
Bibliographic record
Abstract
The consequences of viscous, ohmic, thermal radiation, and chemical reaction on an incompressible, steady, magnetized nanofluid moving through a stretched surface are investigated for choosing two distinct Nanoliquids (Cuo/water and Silver/water). Through the similarity transformations, the controlling dimensional equations of momentum, energy, and concentration were simplified to non-dimensional forms. The solution to the resulting problem is obtained by Bvp4c. The velocity, temperature and concentration are significantly shown for various emerging factors. On both kinds of nanofluids, thermal is grown consistently with an increasing Eckart number. It does, however, lessen as the radiation term values increase. Moreover, if the Schmidt number ranges on the concentration fluid flow indicate that the H(η) profile is going to fall. The tendency for H(η) to boost as the Soret number raises is becoming increasingly noticeable. Ag/water has a higher heat flow rate than Cuo/water when the amounts of Eckart and Radiation numbers are changed. Tables and plots displayed an engineering physical parameter such as coefficient of skin friction, rate of heat transmission and mass transfer.
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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) | 0.001 | 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".