EMHD flow of non-Newtonian nanofluids over thin needle with Robinson’s condition and Arrhenius pre-exponential factor law
Bibliographic record
Abstract
Abstract Many researchers and scientists are devoting their time to scrutinize nanofluids nature and characteristics for heat transfer enhancement. The scrutiny of nanoliquids is important in the large scale thermal management systems via evaporators, advanced cooling systems, heat exchangers, micro/nano-electromechanical devices and industrial chilling applications. Nanoliquids are very momentous even in the natural process via different fields like chemistry, chemical engineering, physics and biology. Nanoliquids can be utilized in various fields of engineering such as different chemical procedures, cooling of electronic equipment and heat exchangers. The main aim of current article is to scrutinize electromagnetohydrodynamic flow of micropolar-Casson-Carreau nanoliquids over thin needle with Robinson’s conditions and Arrhenius pre-exponential factor law. Double stratification effects are also taken into account. The reverent partial differential equations are reformulated into the system of ordinary differential expressions by implementing appropriate transformations. Such obtained equations subject to boundary constraints are computed numerically by considering Runge–Kutta-Fehlberg method. Behaviour of numerous interesting parameters on flow fields is deliberated. The outcomes of flow fields are delineated through graphs and tabular data.
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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 | 0.001 | 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".