Numerical study of higher-order chemical reactions and the Dufour–Soret effect on radiative hybrid nanofluid flow over a stretching curved surface
Bibliographic record
Abstract
Curve-shaped stretching sheets have many notable applications in foam bubbles, molecular films, aerosol drops, and soap films. Hybrid nanofluids exhibit effective heat transmission due to their dual metallic nanoparticles within the base fluid and have a wide range of potential uses. In this article, we investigated the Dufour and Soret effects on hybrid nanofluid flow over a permeable nonlinear stretching curve surface with higher-order chemical reactions and nonlinear solar radiation. We considered the aluminium oxide ( Al2 O3) and graphene as nanoparticles and suspended them in ethylene glycol. The nonlinear leading equations are converted into dimensionless ordinary linear equations by appropriate similarity transformation. The RK-4 shooting method solves the transformed equations, while Mapple-21 simulates the results. Features of the fluid flow are investigated for various parameters, and the findings are displayed using diagrams and charts. The most important findings of this research are the effects of several parameters on the velocity and temperature distribution, as well as the engineering quantities. The value of skin friction was reduced by 63.92% for suction and 64.34% for injection when the radius of curvature increased from 0.2 to 0.6. Additionally, the Nusselt number increased at a rate of 43.39% for suction and 72.72% for injection near the surface as the radiation parameter increased from 0.2 to 0.6. The increasing rate of the Sherwood number is 53.65% for injection, and it drops off by 82.15% for suction when the Soret number increases from 1 to 5.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".