Study of micropolar nanofluids with power-law spin gradient viscosity model by the Keller box method
Bibliographic record
Abstract
The spin gradient viscosity with power-law model and its representation of the heat transfer capabilities of nanofluids have been examined. The theoretical analysis provides an insight into the heat conduction properties of shear-thinning and the shear-thickening fluids. Boundary-layer-approximation-based nonlinear partial differential equations are transformed into nonlinear ordinary differential equations before their solution is approximated by the finite-difference-based Keller box method. The results demonstrate that the heat exchange in nanofluids is affected substantially by the index exponent and the modified material parameter. In addition, the physical quantities of interest from the engineering perspective, the Nusselt and the Sherwood numbers, are calculated to examine the heat and mass transport efficiency of the nanofluids. It is discovered that the temperature profile augments with an increase in the Brownian motion and thermophoresis parameters and decreases with an increase in the Prandtl number and power-law index. However, the concentration deceases with a rise in the Brownian motion parameter and Lewis number, but increases with an increase in the thermophoresis parameter, Prandtl number, and the power-law index.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".