Thermodynamics feature of modified non-Newtonian fluid model over an exponentially curved stretching surface
Bibliographic record
Abstract
In this analysis, we have developed a new stress tensor of combined fluid models such as Sutterby fluid, Casson fluid, and micropolar fluid. The study focuses on fluid flow over a curved sheet stretched exponentially. Brownian motion and thermophoresis impacts are highlighted and considered to have a very low magnetic Reynolds number with heat generation effect. Based on certain flow assumptions, the mathematical model has been developed using boundary layer approximations in terms of coupled partial differential equations (PDEs). These PDEs have been transformed from ordinary differential equations and solved using numerical technique. The findings are presented through graphical and tabular data, illustrating the impacts of governing physical parameters on the system. Concentration curves show improving phenomena by enlarging values of the curvature factor. The Casson fluid parameter significantly impacts mass and heat transfer, resulting in higher rates while simultaneously reducing friction at the surface. The range of the physical factors is presented as 0.1 ≤ β ≤ 6.0, 0.0 ≤ Υ ≤ 5.0, 0.7 ≤ Pr ≤ 10, 0.0 ≤ δ1 ≤ 4.0, 0.0 ≤ Ec ≤ 5.0, 0.0 ≤ P ≤ 10.0, 0.0 ≤ M ≤ 5.0, 0.0 ≤ γ1 ≤ 4.0, 0.0 ≤ Nt ≤ 3, and 0.0 ≤ Nb ≤ 3. The velocity becomes lesser due to increasing values of the Casson fluid factor and of the Sutterby fluid factor. The velocity revealed declining due to enrichment in the micropolar fluid parameter.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".