Exploring Sutterby fluid flow over a stretched surface in porous media with non-Newtonian dissipation and Cattaneo–Christov heat/mass flux models
Bibliographic record
Abstract
This research holds significance in the fields of materials science, chemical engineering, and biomedical engineering, specifically in terms of enhancing procedures such as medication delivery and polymer processing. Therefore, this study involves the development of mathematical model and the subsequent numerical analysis of the flow of non-Newtonian fluid. Here, the non-Newtonian characteristics are represented by employing the Sutterby fluid model. This analysis considers the influence of radiant heat and takes into account the phenomenon of viscous dissipation. The movement of the fluid arises as a result of the stretching of a surface within a saturated porous material, employing the Cattaneo–Christov model to describe the diffusion of heat. The flow governing equations include the effects of variable viscosity and variable thermal conductivity through the utilization of the Sutterby model. The proposed model is mathematically constructed using fundamental partial differential equations that describe the conservation of mass, momentum, and energy. This formulation is grounded in the principles of boundary layer theory. We transformed the governing equations into ordinary differential equations by employing a similarity variables approach. The shooting approach is utilized for numerical analysis of the governing equations in the Sutterby model. The influences of various defining parameters on velocity and temperature profiles are established and examined using graphical representations. The results were compared with prior research, and a high degree of concurrence was noted. The key primary findings we draw from our research indicate the following trend: the temperature and concentration profiles in the fluid system are much improved by raising the viscosity parameter, the porosity parameter, and no-suction condition.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".