Insight into 3-D Darcy-Forchheimer micropolar fluid flow over a nonlinear elongated sheet
Bibliographic record
Abstract
This research investigates the effects of thermal emission, temperature-dependent thermal energy generation/absorption under the application of an inclined magnetic force field on a 3-D micropolar Darcy-Forchheimer stream resting on a convectively heated and nonlinearly elongated sheet with wall slip. The study aims to understand how these factors influence fluid dynamics and heat transfer characteristics in this complex system. To accomplish this, adapting scaling analysis, the set of partial differential equations (PDEs) representing the physics of the problem is altered into a system of ordinary nonlinear differential equations (ODEs). The consequential ODEs are solved utilizing the shooting mechanism in conjunction with the Runge-Kutta Fehlberg algorithm. The visualization of results is discussed eliciting the impact of different parameters emerged in the analysis. It is perceived that the velocity distribution across the fluid is enhanced by the material parameter (β) and the thermal Grashof number (Gr). Augmentation of Forchheimer number (Fr) and porosity parameter (K) has a declining influence on velocities and the x and y components of drag coefficient. The temperature distribution across the fluid region is boosted with radiation parameter and Biot number. The heat transfer rate is positively correlated with the Prandtl number (Pr), while a contrasting effect is observed with the temperature-dependent heat source/sink (Q).
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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 | 0.000 |
| 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".