Role of Thermal Radiations in MHD Micropolar Nanofluid Flow over a Stretching/Shrinking Surface: Triple Solutions with Stability Analysis
Bibliographic record
Abstract
Enhancing thermal efficiency is one of the best strategies for optimizing energy resources. As a result, researchers have been working hard to develop novel ways to maximize the results of energy use. Researchers are becoming more and more interested in nanofluids because of their distinctive thermophysical characteristics and potential uses in thermal engineering systems, heating and cooling processes, nanotechnology, and biomedicine. This study presents a numerical investigation of heat and mass transfer analysis of micropolar nanofluid flow over a stretching/shrinking surface, by incorporating an inclined magnetic field, chemical reaction, and Soret effects. A suitable methodology is adopted to transform the governing boundary layer equations of fluid flow into dimensionless nonlinear ODEs. The stability analysis method is used to resolve coupled nonlinear differential equations with MATLAB software using the Bvp4c solver. Graphs are utilized to illustrate how dimensionless physical factors affect the velocity, temperature, and concentration patterns. It was concluded that increasing the values of the radiation parameter caused a decline in the temperature profile, whereas an increment in the Soret factor enhanced the temperature profile.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".